Pub Date : 2024-06-13DOI: 10.5194/gmd-17-4673-2024
W. Walters, Masayuki Takeuchi, N. L. Ng, Meredith G. Hastings
Abstract. The oxygen isotope anomaly (Δ17O = δ17O − 0.52 × δ18O > 0) has proven to be a robust tool for probing photochemical cycling and atmospheric formation pathways of oxidized reactive nitrogen (NOy). Several studies have developed modeling techniques to implicitly model Δ17O of NOy molecules based on numerous assumptions that may not always be valid. Thus, these models may be oversimplified and limit our ability to compare model Δ17O values of NOy with observations. In this work, we introduce a novel method for explicitly tracking Δ17O transfer and propagation into NOy and odd oxygen (Ox), integrated into the Regional Atmospheric Chemistry Mechanism, version 2 (RACM2). Termed ICOIN-RACM2 (InCorporating Oxygen Isotopes of NOy in RACM2), this new model includes the addition of 55 new species and 729 replicate reactions to represent the propagation of Δ17O derived from O3 into NOy and Ox. Employing this mechanism within a box model, we simulate Δ17O for various NOy and Ox molecules for chamber experiments with varying initial nitrogen oxides (NOx = NO + NO2) and α-pinene conditions, revealing response shifts in Δ17O linked to distinct oxidant conditions. Furthermore, diel cycles are simulated under two summertime scenarios, representative of an urban and rural site, revealing pronounced Δ17O diurnal patterns for several NOy components and substantial Δ17O differences associated with pollution levels (urban vs. rural). Overall, the proposed mechanism offers the potential to assess NOy oxidation chemistry in chamber studies and air quality campaigns through Δ17O model comparisons against observations. The integration of this mechanism into a 3-D atmospheric chemistry transport model is expected to notably enhance our capacity to model and anticipate Δ17O across landscapes, consequently refining model representations of atmospheric chemistry and tropospheric oxidation capacity.
{"title":"Incorporating Oxygen Isotopes of Oxidized Reactive Nitrogen in the Regional Atmospheric Chemistry Mechanism, version 2 (ICOIN-RACM2)","authors":"W. Walters, Masayuki Takeuchi, N. L. Ng, Meredith G. Hastings","doi":"10.5194/gmd-17-4673-2024","DOIUrl":"https://doi.org/10.5194/gmd-17-4673-2024","url":null,"abstract":"Abstract. The oxygen isotope anomaly (Δ17O = δ17O − 0.52 × δ18O > 0) has proven to be a robust tool for probing photochemical cycling and atmospheric formation pathways of oxidized reactive nitrogen (NOy). Several studies have developed modeling techniques to implicitly model Δ17O of NOy molecules based on numerous assumptions that may not always be valid. Thus, these models may be oversimplified and limit our ability to compare model Δ17O values of NOy with observations. In this work, we introduce a novel method for explicitly tracking Δ17O transfer and propagation into NOy and odd oxygen (Ox), integrated into the Regional Atmospheric Chemistry Mechanism, version 2 (RACM2). Termed ICOIN-RACM2 (InCorporating Oxygen Isotopes of NOy in RACM2), this new model includes the addition of 55 new species and 729 replicate reactions to represent the propagation of Δ17O derived from O3 into NOy and Ox. Employing this mechanism within a box model, we simulate Δ17O for various NOy and Ox molecules for chamber experiments with varying initial nitrogen oxides (NOx = NO + NO2) and α-pinene conditions, revealing response shifts in Δ17O linked to distinct oxidant conditions. Furthermore, diel cycles are simulated under two summertime scenarios, representative of an urban and rural site, revealing pronounced Δ17O diurnal patterns for several NOy components and substantial Δ17O differences associated with pollution levels (urban vs. rural). Overall, the proposed mechanism offers the potential to assess NOy oxidation chemistry in chamber studies and air quality campaigns through Δ17O model comparisons against observations. The integration of this mechanism into a 3-D atmospheric chemistry transport model is expected to notably enhance our capacity to model and anticipate Δ17O across landscapes, consequently refining model representations of atmospheric chemistry and tropospheric oxidation capacity.\u0000","PeriodicalId":12799,"journal":{"name":"Geoscientific Model Development","volume":null,"pages":null},"PeriodicalIF":5.1,"publicationDate":"2024-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141347122","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-13DOI: 10.5194/gmd-17-4643-2024
J. Shuman, R. Fisher, Charles D. Koven, Ryan Knox, Lara Kueppers, Chonggang Xu
Abstract. Fire is a fundamental part of the Earth system, with impacts on vegetation structure, biomass, and community composition, the latter mediated in part via key fire-tolerance traits, such as bark thickness. Due to anthropogenic climate change and land use pressure, fire regimes are changing across the world, and fire risk has already increased across much of the tropics. Projecting the impacts of these changes at global scales requires that we capture the selective force of fire on vegetation distribution through vegetation functional traits and size structure. We have adapted the fire behavior and effects module, SPITFIRE (SPread and InTensity of FIRE), for use with the Functionally Assembled Terrestrial Ecosystem Simulator (FATES), a size-structured vegetation demographic model. We test how climate, fire regime, and fire-tolerance plant traits interact to determine the biogeography of tropical forests and grasslands. We assign different fire-tolerance strategies based on crown, leaf, and bark characteristics, which are key observed fire-tolerance traits across woody plants. For these simulations, three types of vegetation compete for resources: a fire-vulnerable tree with thin bark, a vulnerable deep crown, and fire-intolerant foliage; a fire-tolerant tree with thick bark, a thin crown, and fire-tolerant foliage; and a fire-promoting C4 grass. We explore the model sensitivity to a critical parameter governing fuel moisture and show that drier fuels promote increased burning, an expansion of area for grass and fire-tolerant trees, and a reduction of area for fire-vulnerable trees. This conversion to lower biomass or grass areas with increased fuel drying results in increased fire-burned area and its effects, which could feed back to local climate variables. Simulated size-based fire mortality for trees less than 20 cm in diameter and those with fire-vulnerable traits is higher than that for larger and/or fire-tolerant trees, in agreement with observations. Fire-disturbed forests demonstrate reasonable productivity and capture observed patterns of aboveground biomass in areas dominated by natural vegetation for the recent historical period but have a large bias in less disturbed areas. Though the model predicts a greater extent of burned fraction than observed in areas with grass dominance, the resulting biogeography of fire-tolerant, thick-bark trees and fire-vulnerable, thin-bark trees corresponds to observations across the tropics. In areas with more than 2500 mm of precipitation, simulated fire frequency and burned area are low, with fire intensities below 150 kW m−1, consistent with observed understory fire behavior across the Amazon. Areas drier than this demonstrate fire intensities consistent with those measured in savannas and grasslands, with high values up to 4000 kW m−1. The results support a positive grass–fire feedback across the region and suggest that forests which have existed without frequent burning may be vulnerable at
{"title":"Dynamic ecosystem assembly and escaping the “fire trap” in the tropics: insights from FATES_15.0.0","authors":"J. Shuman, R. Fisher, Charles D. Koven, Ryan Knox, Lara Kueppers, Chonggang Xu","doi":"10.5194/gmd-17-4643-2024","DOIUrl":"https://doi.org/10.5194/gmd-17-4643-2024","url":null,"abstract":"Abstract. Fire is a fundamental part of the Earth system, with impacts on vegetation structure, biomass, and community composition, the latter mediated in part via key fire-tolerance traits, such as bark thickness. Due to anthropogenic climate change and land use pressure, fire regimes are changing across the world, and fire risk has already increased across much of the tropics. Projecting the impacts of these changes at global scales requires that we capture the selective force of fire on vegetation distribution through vegetation functional traits and size structure. We have adapted the fire behavior and effects module, SPITFIRE (SPread and InTensity of FIRE), for use with the Functionally Assembled Terrestrial Ecosystem Simulator (FATES), a size-structured vegetation demographic model. We test how climate, fire regime, and fire-tolerance plant traits interact to determine the biogeography of tropical forests and grasslands. We assign different fire-tolerance strategies based on crown, leaf, and bark characteristics, which are key observed fire-tolerance traits across woody plants. For these simulations, three types of vegetation compete for resources: a fire-vulnerable tree with thin bark, a vulnerable deep crown, and fire-intolerant foliage; a fire-tolerant tree with thick bark, a thin crown, and fire-tolerant foliage; and a fire-promoting C4 grass. We explore the model sensitivity to a critical parameter governing fuel moisture and show that drier fuels promote increased burning, an expansion of area for grass and fire-tolerant trees, and a reduction of area for fire-vulnerable trees. This conversion to lower biomass or grass areas with increased fuel drying results in increased fire-burned area and its effects, which could feed back to local climate variables. Simulated size-based fire mortality for trees less than 20 cm in diameter and those with fire-vulnerable traits is higher than that for larger and/or fire-tolerant trees, in agreement with observations. Fire-disturbed forests demonstrate reasonable productivity and capture observed patterns of aboveground biomass in areas dominated by natural vegetation for the recent historical period but have a large bias in less disturbed areas. Though the model predicts a greater extent of burned fraction than observed in areas with grass dominance, the resulting biogeography of fire-tolerant, thick-bark trees and fire-vulnerable, thin-bark trees corresponds to observations across the tropics. In areas with more than 2500 mm of precipitation, simulated fire frequency and burned area are low, with fire intensities below 150 kW m−1, consistent with observed understory fire behavior across the Amazon. Areas drier than this demonstrate fire intensities consistent with those measured in savannas and grasslands, with high values up to 4000 kW m−1. The results support a positive grass–fire feedback across the region and suggest that forests which have existed without frequent burning may be vulnerable at","PeriodicalId":12799,"journal":{"name":"Geoscientific Model Development","volume":null,"pages":null},"PeriodicalIF":5.1,"publicationDate":"2024-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141348618","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-13DOI: 10.5194/gmd-17-4689-2024
G. Lazoglou, Theo Economou, Christina Anagnostopoulou, G. Zittis, Anna Tzyrkalli, Pantelis Georgiades, J. Lelieveld
Abstract. Precipitation holds significant importance as a climate parameter in various applications, including studies on the impacts of climate change. However, its simulation or projection accuracy is low, primarily due to its high stochasticity. Specifically, climate models often overestimate the frequency of light rainy days while simultaneously underestimating the total amounts of extreme observed precipitation. This phenomenon, known as “drizzle bias”, specifically refers to the model's tendency to overestimate the occurrence of light precipitation events. Consequently, even though the overall precipitation totals are generally well represented, there is often a significant bias in the number of rainy days. The present study aims to minimize the drizzle bias in model output by developing and applying two statistical approaches. In the first approach, the number of rainy days is adjusted based on the assumption that the relationship between observed and simulated rainy days remains the same in time (thresholding). In the second, a machine learning method (random forest or RF) is used for the development of a statistical model that describes the relationship between several climate (modelled) variables and the observed number of wet days. The results demonstrate that employing a multivariate approach yields results that are comparable to the conventional thresholding approach when correcting sub-periods with similar climate characteristics. However, the importance of utilizing RF becomes evident when addressing periods exhibiting extreme events, marked by a significantly distinct frequency of rainy days. These disparities are particularly pronounced when considering higher temporal resolutions. Both methods are illustrated on data from three EURO-CORDEX climate models. The two approaches are trained during a calibration period, and they are applied for the selected evaluation period.
{"title":"Multivariate adjustment of drizzle bias using machine learning in European climate projections","authors":"G. Lazoglou, Theo Economou, Christina Anagnostopoulou, G. Zittis, Anna Tzyrkalli, Pantelis Georgiades, J. Lelieveld","doi":"10.5194/gmd-17-4689-2024","DOIUrl":"https://doi.org/10.5194/gmd-17-4689-2024","url":null,"abstract":"Abstract. Precipitation holds significant importance as a climate parameter in various applications, including studies on the impacts of climate change. However, its simulation or projection accuracy is low, primarily due to its high stochasticity. Specifically, climate models often overestimate the frequency of light rainy days while simultaneously underestimating the total amounts of extreme observed precipitation. This phenomenon, known as “drizzle bias”, specifically refers to the model's tendency to overestimate the occurrence of light precipitation events. Consequently, even though the overall precipitation totals are generally well represented, there is often a significant bias in the number of rainy days. The present study aims to minimize the drizzle bias in model output by developing and applying two statistical approaches. In the first approach, the number of rainy days is adjusted based on the assumption that the relationship between observed and simulated rainy days remains the same in time (thresholding). In the second, a machine learning method (random forest or RF) is used for the development of a statistical model that describes the relationship between several climate (modelled) variables and the observed number of wet days. The results demonstrate that employing a multivariate approach yields results that are comparable to the conventional thresholding approach when correcting sub-periods with similar climate characteristics. However, the importance of utilizing RF becomes evident when addressing periods exhibiting extreme events, marked by a significantly distinct frequency of rainy days. These disparities are particularly pronounced when considering higher temporal resolutions. Both methods are illustrated on data from three EURO-CORDEX climate models. The two approaches are trained during a calibration period, and they are applied for the selected evaluation period.\u0000","PeriodicalId":12799,"journal":{"name":"Geoscientific Model Development","volume":null,"pages":null},"PeriodicalIF":5.1,"publicationDate":"2024-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141346994","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-12DOI: 10.5194/gmd-17-4621-2024
Xu Yue, Hao Zhou, Chenguang Tian, Yimian Ma, Yihan Hu, C. Gong, Hui Zheng, Hong Liao
Abstract. Land ecosystems are important sources and sinks of atmospheric components. In turn, air pollutants affect the exchange rates of carbon and water fluxes between ecosystems and the atmosphere. However, these biogeochemical processes are usually not well presented in Earth system models, limiting the explorations of interactions between land ecosystems and air pollutants from regional to global scales. Here, we develop and validate the interactive Model for Air Pollution and Land Ecosystems (iMAPLE) by upgrading the Yale Interactive Terrestrial Biosphere Model with process-based water cycles, fire emissions, wetland methane (CH4) emissions, and trait-based ozone (O3) damage. Within iMAPLE, soil moisture and temperature are dynamically calculated based on the water and energy balance in soil layers. Fire emissions are dependent on dryness, lightning, population, and fuel load. Wetland CH4 is produced but consumed through oxidation, ebullition, diffusion, and plant-mediated transport. The trait-based scheme unifies O3 sensitivity of different plant functional types (PFTs) with the leaf mass per area. Validations show correlation coefficients (R) of 0.59–0.86 for gross primary productivity (GPP) and 0.57–0.84 for evapotranspiration (ET) across the six PFTs at 201 flux tower sites and yield an average R of 0.68 for CH4 emissions at 44 sites. Simulated soil moisture and temperature match reanalysis data with high R above 0.86 and low normalized mean biases (NMBs) within 7 %, leading to reasonable simulations of global GPP (R=0.92, NMB=1.3 %) and ET (R=0.93, NMB=-10.4 %) against satellite-based observations for 2001–2013. The model predicts an annual global area burned of 507.1 Mha, close to the observations of 475.4 Mha with a spatial R of 0.66 for 1997–2016. The wetland CH4 emissions are estimated to be 153.45 Tg [CH4] yr−1 during 2000–2014, close to the multi-model mean of 148 Tg [CH4] yr−1. The model also shows reasonable responses of GPP and ET to the changes in diffuse radiation and yields mean O3 damage of 2.9 % to global GPP. iMAPLE provides an advanced tool for studying the interactions between land ecosystems and air pollutants.
{"title":"Development and evaluation of the interactive Model for Air Pollution and Land Ecosystems (iMAPLE) version 1.0","authors":"Xu Yue, Hao Zhou, Chenguang Tian, Yimian Ma, Yihan Hu, C. Gong, Hui Zheng, Hong Liao","doi":"10.5194/gmd-17-4621-2024","DOIUrl":"https://doi.org/10.5194/gmd-17-4621-2024","url":null,"abstract":"Abstract. Land ecosystems are important sources and sinks of atmospheric components. In turn, air pollutants affect the exchange rates of carbon and water fluxes between ecosystems and the atmosphere. However, these biogeochemical processes are usually not well presented in Earth system models, limiting the explorations of interactions between land ecosystems and air pollutants from regional to global scales. Here, we develop and validate the interactive Model for Air Pollution and Land Ecosystems (iMAPLE) by upgrading the Yale Interactive Terrestrial Biosphere Model with process-based water cycles, fire emissions, wetland methane (CH4) emissions, and trait-based ozone (O3) damage. Within iMAPLE, soil moisture and temperature are dynamically calculated based on the water and energy balance in soil layers. Fire emissions are dependent on dryness, lightning, population, and fuel load. Wetland CH4 is produced but consumed through oxidation, ebullition, diffusion, and plant-mediated transport. The trait-based scheme unifies O3 sensitivity of different plant functional types (PFTs) with the leaf mass per area. Validations show correlation coefficients (R) of 0.59–0.86 for gross primary productivity (GPP) and 0.57–0.84 for evapotranspiration (ET) across the six PFTs at 201 flux tower sites and yield an average R of 0.68 for CH4 emissions at 44 sites. Simulated soil moisture and temperature match reanalysis data with high R above 0.86 and low normalized mean biases (NMBs) within 7 %, leading to reasonable simulations of global GPP (R=0.92, NMB=1.3 %) and ET (R=0.93, NMB=-10.4 %) against satellite-based observations for 2001–2013. The model predicts an annual global area burned of 507.1 Mha, close to the observations of 475.4 Mha with a spatial R of 0.66 for 1997–2016. The wetland CH4 emissions are estimated to be 153.45 Tg [CH4] yr−1 during 2000–2014, close to the multi-model mean of 148 Tg [CH4] yr−1. The model also shows reasonable responses of GPP and ET to the changes in diffuse radiation and yields mean O3 damage of 2.9 % to global GPP. iMAPLE provides an advanced tool for studying the interactions between land ecosystems and air pollutants.\u0000","PeriodicalId":12799,"journal":{"name":"Geoscientific Model Development","volume":null,"pages":null},"PeriodicalIF":5.1,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141353719","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-12DOI: 10.5194/gmd-17-4603-2024
S. Winkelbauer, Michael Mayer, L. Haimberger
Abstract. Oceanic transports shape the global climate, but the evaluation and validation of this key quantity based on reanalysis and model data are complicated by the distortion of the used curvilinear ocean model grids towards their displaced north poles. Combined with the large number of different grid types, this has made the exact calculation of oceanic transports a challenging and time-consuming task. Use of data interpolated to standard latitude/longitude grids is not an option, since transports computed from interpolated velocity fields are not mass-consistent. We present two methods for transport calculations on grids with variously shifted north poles, different orientations, and different Arakawa partitions. The first method calculates net transports through arbitrary sections using line integrals, while the second method generates cross sections of the vertical–horizontal planes of these sections using vector projection algorithms. Apart from the input data on the original model grids, the user only needs to specify the start and endpoints of the required section to get the net transports (for the first method) and their cross sections (for the second method). Integration of the cross sections along their depth and horizontal extent yields net transports in very good quantitative agreement with the line integration method. This allows us to calculate oceanic fluxes through almost arbitrary sections to compare them with observed oceanic volume and energy transports at available sections, such as the RAPID array or at Fram Strait and other Arctic gateways, or to compare them amongst reanalyses and to model integrations from the Coupled Model Intercomparison Projects (CMIPs). We implemented our methods in a Python package called StraitFlux. This paper represents its scientific documentation and demonstrates its application on outputs of multiple CMIP6 models and several ocean reanalyses. We also analyze the robustness and computational performance of the tools, as well as the uncertainties in the results. The package is available on GitHub and Zenodo and can be installed using pypi.
{"title":"StraitFlux – precise computations of water strait fluxes on various modeling grids","authors":"S. Winkelbauer, Michael Mayer, L. Haimberger","doi":"10.5194/gmd-17-4603-2024","DOIUrl":"https://doi.org/10.5194/gmd-17-4603-2024","url":null,"abstract":"Abstract. Oceanic transports shape the global climate, but the evaluation and validation of this key quantity based on reanalysis and model data are complicated by the distortion of the used curvilinear ocean model grids towards their displaced north poles. Combined with the large number of different grid types, this has made the exact calculation of oceanic transports a challenging and time-consuming task. Use of data interpolated to standard latitude/longitude grids is not an option, since transports computed from interpolated velocity fields are not mass-consistent. We present two methods for transport calculations on grids with variously shifted north poles, different orientations, and different Arakawa partitions. The first method calculates net transports through arbitrary sections using line integrals, while the second method generates cross sections of the vertical–horizontal planes of these sections using vector projection algorithms. Apart from the input data on the original model grids, the user only needs to specify the start and endpoints of the required section to get the net transports (for the first method) and their cross sections (for the second method). Integration of the cross sections along their depth and horizontal extent yields net transports in very good quantitative agreement with the line integration method. This allows us to calculate oceanic fluxes through almost arbitrary sections to compare them with observed oceanic volume and energy transports at available sections, such as the RAPID array or at Fram Strait and other Arctic gateways, or to compare them amongst reanalyses and to model integrations from the Coupled Model Intercomparison Projects (CMIPs). We implemented our methods in a Python package called StraitFlux. This paper represents its scientific documentation and demonstrates its application on outputs of multiple CMIP6 models and several ocean reanalyses. We also analyze the robustness and computational performance of the tools, as well as the uncertainties in the results. The package is available on GitHub and Zenodo and can be installed using pypi.\u0000","PeriodicalId":12799,"journal":{"name":"Geoscientific Model Development","volume":null,"pages":null},"PeriodicalIF":5.1,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141351854","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract. With the development of refined numerical forecasts, problems such as score distortion due to the division of precipitation thresholds in both traditional and improved scoring methods for precipitation forecasts and the increasing subjective risk arising from the scale setting of the neighborhood spatial verification method have become increasingly prominent. To address these issues, a general comprehensive evaluation method (GCEM) is developed for cross-scale precipitation forecasts by directly analyzing the proximity of precipitation forecasts and observations in this study. In addition to the core indicator of the precipitation accuracy score (PAS), the GCEM system also includes score indices for insufficient precipitation forecasts, excessive precipitation forecasts, precipitation forecast biases, and clear/rainy forecasts. The PAS does not distinguish the magnitude of precipitation and does not delimit the area of influence; it constitutes a fair scoring formula with objective performance and can be suitable for evaluating rainfall events such as general and extreme precipitation. The PAS can be used to calculate the accuracy of numerical models or quantitative precipitation forecasts, enabling the quantitative evaluation of the comprehensive capability of various refined precipitation forecasting products. Based on the GCEM, comparative experiments between the PAS and threat score (TS) are conducted for two typical precipitation weather processes. The results show that relative to the TS, the PAS better aligns with subjective expectations, indicating that the PAS is more reasonable than the TS. In the case of an extreme-precipitation event in Henan, China, two high-resolution models were evaluated using the PAS, TS, and fraction skill score (FSS), verifying the evaluation ability of PAS scoring for predicting extreme-precipitation events. In addition, other indices of the GCEM are utilized to analyze the range and extent of both insufficient and excessive forecasts of precipitation, as well as the precipitation forecasting ability for different weather processes. These indices not only provide overall scores similar to those of the TS for individual cases but also support two-dimensional score distribution plots which can comprehensively reflect the performance and characteristics of precipitation forecasts. Both theoretical and practical applications demonstrate that the GCEM exhibits distinct advantages and potential promotion and application value compared to the various mainstream precipitation forecast verification methods.
摘要随着精细化数值预报的发展，传统的降水预报评分方法和改进的降水预报评分方法中由于降水阈值划分导致的评分失真、邻域空间验证方法的尺度设置导致的主观风险增大等问题日益突出。针对这些问题，本研究通过直接分析降水预报与观测资料的接近程度，建立了跨尺度降水预报的一般综合评价方法（GCEM）。除了降水准确性评分（PAS）这一核心指标外，GCEM 系统还包括降水预报不足、降水预报过多、降水预报偏差和晴雨预报等评分指标。PAS 不区分降水量的大小，也不划定影响范围；它是一个具有客观性能的公平评分公式，适用于评估一般降水和极端降水等降水事件。PAS 可用于计算数值模式或定量降水预报的准确性，从而对各种精细化降水预报产品的综合能力进行定量评估。在 GCEM 的基础上，针对两种典型的降水天气过程进行了 PAS 和威胁评分（TS）的对比实验。结果表明，相对于 TS，PAS 更符合主观预期，表明 PAS 比 TS 更合理。以中国河南的一次极端降水事件为例，利用 PAS、TS 和分数技能得分（FSS）对两个高分辨率模型进行了评估，验证了 PAS 评分对预测极端降水事件的评估能力。此外，还利用 GCEM 的其他指数分析了降水预报不足和过度的范围和程度，以及不同天气过程的降水预报能力。这些指数不仅提供了与 TS 指数类似的单个案例的总体评分，而且支持二维评分分布图，能够全面反映降水预报的性能和特点。理论和实际应用均表明，与各种主流降水预报验证方法相比，GCEM具有明显的优势和潜在的推广应用价值。
{"title":"A general comprehensive evaluation method for cross-scale precipitation forecasts","authors":"Bing Zhang, Mingjian Zeng, Anning Huang, Zhengkun Qin, Couhua Liu, Wenru Shi, Xin Li, Kefeng Zhu, Chun-Lai Gu, Jialing Zhou","doi":"10.5194/gmd-17-4579-2024","DOIUrl":"https://doi.org/10.5194/gmd-17-4579-2024","url":null,"abstract":"Abstract. With the development of refined numerical forecasts, problems such as score distortion due to the division of precipitation thresholds in both traditional and improved scoring methods for precipitation forecasts and the increasing subjective risk arising from the scale setting of the neighborhood spatial verification method have become increasingly prominent. To address these issues, a general comprehensive evaluation method (GCEM) is developed for cross-scale precipitation forecasts by directly analyzing the proximity of precipitation forecasts and observations in this study. In addition to the core indicator of the precipitation accuracy score (PAS), the GCEM system also includes score indices for insufficient precipitation forecasts, excessive precipitation forecasts, precipitation forecast biases, and clear/rainy forecasts. The PAS does not distinguish the magnitude of precipitation and does not delimit the area of influence; it constitutes a fair scoring formula with objective performance and can be suitable for evaluating rainfall events such as general and extreme precipitation. The PAS can be used to calculate the accuracy of numerical models or quantitative precipitation forecasts, enabling the quantitative evaluation of the comprehensive capability of various refined precipitation forecasting products. Based on the GCEM, comparative experiments between the PAS and threat score (TS) are conducted for two typical precipitation weather processes. The results show that relative to the TS, the PAS better aligns with subjective expectations, indicating that the PAS is more reasonable than the TS. In the case of an extreme-precipitation event in Henan, China, two high-resolution models were evaluated using the PAS, TS, and fraction skill score (FSS), verifying the evaluation ability of PAS scoring for predicting extreme-precipitation events. In addition, other indices of the GCEM are utilized to analyze the range and extent of both insufficient and excessive forecasts of precipitation, as well as the precipitation forecasting ability for different weather processes. These indices not only provide overall scores similar to those of the TS for individual cases but also support two-dimensional score distribution plots which can comprehensively reflect the performance and characteristics of precipitation forecasts. Both theoretical and practical applications demonstrate that the GCEM exhibits distinct advantages and potential promotion and application value compared to the various mainstream precipitation forecast verification methods.\u0000","PeriodicalId":12799,"journal":{"name":"Geoscientific Model Development","volume":null,"pages":null},"PeriodicalIF":5.1,"publicationDate":"2024-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141362682","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-10DOI: 10.5194/gmd-17-4561-2024
T. Hallouin, F. Bourgin, C. Perrin, Maria-Helena Ramos, V. Andréassian
Abstract. The evaluation of streamflow predictions forms an essential part of most hydrological modelling studies published in the literature. The evaluation process typically involves the computation of some evaluation metrics, but it can also involve the preliminary processing of the predictions as well as the subsequent processing of the computed metrics. In order for published hydrological studies to be reproducible, these steps need to be carefully documented by the authors. The availability of a single tool performing all of these tasks would simplify not only the documentation by the authors but also the reproducibility by the readers. However, this requires such a tool to be polyglot (i.e. usable in a variety of programming languages) and openly accessible so that it can be used by everyone in the hydrological community. To this end, we developed a new tool named evalhyd that offers metrics and functionalities for the evaluation of deterministic and probabilistic streamflow predictions. It is open source, and it can be used in Python, in R, in C++, or as a command line tool. This article describes the tool and illustrates its functionalities using Global Flood Awareness System (GloFAS) reforecasts over France as an example data set.
{"title":"EvalHyd v0.1.2: a polyglot tool for the evaluation of deterministic and probabilistic streamflow predictions","authors":"T. Hallouin, F. Bourgin, C. Perrin, Maria-Helena Ramos, V. Andréassian","doi":"10.5194/gmd-17-4561-2024","DOIUrl":"https://doi.org/10.5194/gmd-17-4561-2024","url":null,"abstract":"Abstract. The evaluation of streamflow predictions forms an essential part of most hydrological modelling studies published in the literature. The evaluation process typically involves the computation of some evaluation metrics, but it can also involve the preliminary processing of the predictions as well as the subsequent processing of the computed metrics. In order for published hydrological studies to be reproducible, these steps need to be carefully documented by the authors. The availability of a single tool performing all of these tasks would simplify not only the documentation by the authors but also the reproducibility by the readers. However, this requires such a tool to be polyglot (i.e. usable in a variety of programming languages) and openly accessible so that it can be used by everyone in the hydrological community. To this end, we developed a new tool named evalhyd that offers metrics and functionalities for the evaluation of deterministic and probabilistic streamflow predictions. It is open source, and it can be used in Python, in R, in C++, or as a command line tool. This article describes the tool and illustrates its functionalities using Global Flood Awareness System (GloFAS) reforecasts over France as an example data set.\u0000","PeriodicalId":12799,"journal":{"name":"Geoscientific Model Development","volume":null,"pages":null},"PeriodicalIF":5.1,"publicationDate":"2024-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141363630","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-05DOI: 10.5194/gmd-17-4533-2024
M. Meinshausen, C. Schleussner, Kathleen Beyer, Greg Bodeker, Olivier Boucher, J. Canadell, John S. Daniel, Aïda Diongue-Niang, Fatima Driouech, Erich Fischer, P. Forster, Michael Grose, Gerrit Hansen, Z. Hausfather, T. Ilyina, J. Kikstra, Joyce Kimutai, A. King, June-Yi Lee, Chris Lennard, T. Lissner, A. Nauels, Glen P. Peters, Anna Pirani, G. Plattner, Hans-Ove Pörtner, J. Rogelj, Maisa Rojas, Joyashree Roy, B. Samset, Benjamin M. Sanderson, R. Séférian, S. Seneviratne, Christopher J. Smith, S. Szopa, Adelle Thomas, D. Urge-Vorsatz, G. Velders, T. Yokohata, T. Ziehn, Zebedee R. J. Nicholls
Abstract. In every Intergovernmental Panel on Climate Change (IPCC) Assessment cycle, a multitude of scenarios are assessed, with different scope and emphasis throughout the various Working Group reports and special reports, as well as their respective chapters. Within the reports, the ambition is to integrate knowledge on possible climate futures across the Working Groups and scientific research domains based on a small set of “framing pathways” such as the so-called representative concentration pathways (RCPs) in the Fifth IPCC Assessment Report (AR5) and the shared socioeconomic pathway (SSP) scenarios in the Sixth Assessment Report (AR6). This perspective, initiated by discussions at the IPCC Bangkok workshop in April 2023 on the “Use of Scenarios in AR6 and Subsequent Assessments”, is intended to serve as one of the community contributions to highlight the needs for the next generation of framing pathways that is being advanced under the Coupled Model Intercomparison Project (CMIP) umbrella, which will influence or even predicate the IPCC AR7 consideration of framing pathways. Here we suggest several policy research objectives that such a set of framing pathways should ideally fulfil, including mitigation needs for meeting the Paris Agreement objectives, the risks associated with carbon removal strategies, the consequences of delay in enacting that mitigation, guidance for adaptation needs, loss and damage, and for achieving mitigation in the wider context of societal development goals. Based on this context, we suggest that the next generation of climate scenarios for Earth system models should evolve towards representative emission pathways (REPs) and suggest key categories for such pathways. These framing pathways should address the most critical mitigation policy and adaptation plans that need to be implemented over the next 10 years. In our view, the most important categories are those relevant in the context of the Paris Agreement long-term goal, specifically an immediate action (low overshoot) 1.5 °C pathway and a delayed action (high overshoot) 1.5 °C pathway. Two other key categories are a pathway category approximately in line with current (as expressed by 2023) near- and long-term policy objectives, as well as a higher-emission category that is approximately in line with “current policies” (as expressed by 2023). We also argue for the scientific and policy relevance in exploring two “worlds that could have been”. One of these categories has high-emission trajectories well above what is implied by current policies and the other has very-low-emission trajectories which assume that global mitigation action in line with limiting warming to 1.5 °C without overshoot had begun in 2015. Finally, we note that the timely provision of new scientific information on pathways is critical to inform the development and implementation of climate policy. Under the Paris Agreement, for the second global stocktake, which will occur in 2028, and to in
{"title":"A perspective on the next generation of Earth system model scenarios: towards representative emission pathways (REPs)","authors":"M. Meinshausen, C. Schleussner, Kathleen Beyer, Greg Bodeker, Olivier Boucher, J. Canadell, John S. Daniel, Aïda Diongue-Niang, Fatima Driouech, Erich Fischer, P. Forster, Michael Grose, Gerrit Hansen, Z. Hausfather, T. Ilyina, J. Kikstra, Joyce Kimutai, A. King, June-Yi Lee, Chris Lennard, T. Lissner, A. Nauels, Glen P. Peters, Anna Pirani, G. Plattner, Hans-Ove Pörtner, J. Rogelj, Maisa Rojas, Joyashree Roy, B. Samset, Benjamin M. Sanderson, R. Séférian, S. Seneviratne, Christopher J. Smith, S. Szopa, Adelle Thomas, D. Urge-Vorsatz, G. Velders, T. Yokohata, T. Ziehn, Zebedee R. J. Nicholls","doi":"10.5194/gmd-17-4533-2024","DOIUrl":"https://doi.org/10.5194/gmd-17-4533-2024","url":null,"abstract":"Abstract. In every Intergovernmental Panel on Climate Change (IPCC) Assessment cycle, a multitude of scenarios are assessed, with different scope and emphasis throughout the various Working Group reports and special reports, as well as their respective chapters. Within the reports, the ambition is to integrate knowledge on possible climate futures across the Working Groups and scientific research domains based on a small set of “framing pathways” such as the so-called representative concentration pathways (RCPs) in the Fifth IPCC Assessment Report (AR5) and the shared socioeconomic pathway (SSP) scenarios in the Sixth Assessment Report (AR6). This perspective, initiated by discussions at the IPCC Bangkok workshop in April 2023 on the “Use of Scenarios in AR6 and Subsequent Assessments”, is intended to serve as one of the community contributions to highlight the needs for the next generation of framing pathways that is being advanced under the Coupled Model Intercomparison Project (CMIP) umbrella, which will influence or even predicate the IPCC AR7 consideration of framing pathways. Here we suggest several policy research objectives that such a set of framing pathways should ideally fulfil, including mitigation needs for meeting the Paris Agreement objectives, the risks associated with carbon removal strategies, the consequences of delay in enacting that mitigation, guidance for adaptation needs, loss and damage, and for achieving mitigation in the wider context of societal development goals. Based on this context, we suggest that the next generation of climate scenarios for Earth system models should evolve towards representative emission pathways (REPs) and suggest key categories for such pathways. These framing pathways should address the most critical mitigation policy and adaptation plans that need to be implemented over the next 10 years. In our view, the most important categories are those relevant in the context of the Paris Agreement long-term goal, specifically an immediate action (low overshoot) 1.5 °C pathway and a delayed action (high overshoot) 1.5 °C pathway. Two other key categories are a pathway category approximately in line with current (as expressed by 2023) near- and long-term policy objectives, as well as a higher-emission category that is approximately in line with “current policies” (as expressed by 2023). We also argue for the scientific and policy relevance in exploring two “worlds that could have been”. One of these categories has high-emission trajectories well above what is implied by current policies and the other has very-low-emission trajectories which assume that global mitigation action in line with limiting warming to 1.5 °C without overshoot had begun in 2015. Finally, we note that the timely provision of new scientific information on pathways is critical to inform the development and implementation of climate policy. Under the Paris Agreement, for the second global stocktake, which will occur in 2028, and to in","PeriodicalId":12799,"journal":{"name":"Geoscientific Model Development","volume":null,"pages":null},"PeriodicalIF":5.1,"publicationDate":"2024-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141383711","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-24DOI: 10.5194/gmd-17-4311-2024
Felix Wieser, Rolf Sander, C. Cho, H. Fuchs, Torsten Hohaus, A. Novelli, R. Tillmann, D. Taraborrelli
Abstract. During the last few decades, the impact of multiphase chemistry on secondary organic aerosols (SOAs) has been demonstrated to be the key to explaining laboratory experiments and field measurements. However, global atmospheric models still show large biases when simulating atmospheric observations of organic aerosols (OAs). Major reasons for the model errors are the use of simplified chemistry schemes of the gas-phase oxidation of vapours and the parameterization of heterogeneous surface reactions. The photochemical oxidation of anthropogenic and biogenic volatile organic compounds (VOCs) leads to products that either produce new SOA or are taken up by existing aqueous media like cloud droplets and deliquescent aerosols. After partitioning, aqueous-phase processing results in polyols, organosulfates, and other products with a high molar mass and oxygen content. In this work, we introduce the formation of new low-volatility organic compounds (LVOCs) to the multiphase chemistry box model CAABA/MECCA. Most notable are the additions of the SOA precursors, limonene and n-alkanes (5 to 8 C atoms), and a semi-explicit chemical mechanism for the formation of LVOCs from isoprene oxidation in the gas and aqueous phases. Moreover, Henry's law solubility constants and their temperature dependences are estimated for the partitioning of organic molecules to the aqueous phase. Box model simulations indicate that the new chemical scheme predicts the enhanced formation of LVOCs, which are known for being precursor species to SOAs. As expected, the model predicts that LVOCs are positively correlated to temperature but negatively correlated to NOx levels. However, the aqueous-phase processing of isoprene epoxydiols (IEPOX) displays a more complex dependence on these two key variables. Semi-quantitative comparison with observations from the SOAS campaign suggests that the model may overestimate methylbutane-1,2,3,4-tetrol (MeBuTETROL) from IEPOX. Further application of the mechanism in the modelling of two chamber experiments, one in which limonene is consumed by ozone and one in which isoprene is consumed by NO3 shows a sufficient agreement with experimental results within model limitations. The extensions in CAABA/MECCA are transferred to the 3D atmospheric model MESSy for a comprehensive evaluation of the impact of aqueous- and/or aerosol-phase chemistry on SOA at a global scale in a follow-up study.
摘要在过去几十年中，多相化学对二次有机气溶胶（SOAs）的影响已被证明是解释实验室实验和实地测量的关键。然而，全球大气模型在模拟大气中的有机气溶胶（OAs）观测结果时仍存在较大偏差。造成模型误差的主要原因是使用了简化的气相蒸汽氧化化学方案和异质表面反应参数化。人为和生物挥发性有机化合物（VOC）的光化学氧化作用会产生新的 SOA，或被现有的水介质（如云滴和潮解气溶胶）吸收。经过分区后，水相处理会产生多元醇、有机硫酸盐和其他具有高摩尔质量和氧含量的产物。在这项工作中，我们在多相化学盒模型 CAABA/MECCA 中引入了新的低挥发性有机化合物 (LVOC) 的形成。最值得注意的是加入了 SOA 前体--柠檬烯和正构烷烃（5 至 8 个 C 原子），以及异戊二烯在气相和水相氧化形成低挥发性有机化合物的半明了化学机制。此外，还估算了有机分子在水相中的亨利定律溶解常数及其温度相关性。方框模型模拟表明，新的化学方案预测 LVOCs 的形成会增强，众所周知，LVOCs 是 SOAs 的前体物种。正如预期的那样，模型预测 LVOC 与温度呈正相关，但与氮氧化物水平呈负相关。不过，异戊二烯环氧二醇（IEPOX）的水相处理与这两个关键变量的关系更为复杂。与 SOAS 活动的观测结果进行半定量比较后发现，该模型可能会高估 IEPOX 产生的甲基丁烷-1,2,3,4-四醇（MeBuTETROL）。将该机制进一步应用于两个室实验的建模中，一个是臭氧消耗柠檬烯的实验，另一个是 NO3 消耗异戊二烯的实验，结果表明在模型限制范围内与实验结果充分吻合。CAABA/MECCA 中的扩展功能被转移到三维大气模型 MESSy 中，以便在后续研究中全面评估全球范围内水相和/或气溶胶相化学对 SOA 的影响。
{"title":"Development of a multiphase chemical mechanism to improve secondary organic aerosol formation in CAABA/MECCA (version 4.7.0)","authors":"Felix Wieser, Rolf Sander, C. Cho, H. Fuchs, Torsten Hohaus, A. Novelli, R. Tillmann, D. Taraborrelli","doi":"10.5194/gmd-17-4311-2024","DOIUrl":"https://doi.org/10.5194/gmd-17-4311-2024","url":null,"abstract":"Abstract. During the last few decades, the impact of multiphase chemistry on secondary organic aerosols (SOAs) has been demonstrated to be the key to explaining laboratory experiments and field measurements. However, global atmospheric models still show large biases when simulating atmospheric observations of organic aerosols (OAs). Major reasons for the model errors are the use of simplified chemistry schemes of the gas-phase oxidation of vapours and the parameterization of heterogeneous surface reactions. The photochemical oxidation of anthropogenic and biogenic volatile organic compounds (VOCs) leads to products that either produce new SOA or are taken up by existing aqueous media like cloud droplets and deliquescent aerosols. After partitioning, aqueous-phase processing results in polyols, organosulfates, and other products with a high molar mass and oxygen content. In this work, we introduce the formation of new low-volatility organic compounds (LVOCs) to the multiphase chemistry box model CAABA/MECCA. Most notable are the additions of the SOA precursors, limonene and n-alkanes (5 to 8 C atoms), and a semi-explicit chemical mechanism for the formation of LVOCs from isoprene oxidation in the gas and aqueous phases. Moreover, Henry's law solubility constants and their temperature dependences are estimated for the partitioning of organic molecules to the aqueous phase. Box model simulations indicate that the new chemical scheme predicts the enhanced formation of LVOCs, which are known for being precursor species to SOAs. As expected, the model predicts that LVOCs are positively correlated to temperature but negatively correlated to NOx levels. However, the aqueous-phase processing of isoprene epoxydiols (IEPOX) displays a more complex dependence on these two key variables. Semi-quantitative comparison with observations from the SOAS campaign suggests that the model may overestimate methylbutane-1,2,3,4-tetrol (MeBuTETROL) from IEPOX. Further application of the mechanism in the modelling of two chamber experiments, one in which limonene is consumed by ozone and one in which isoprene is consumed by NO3 shows a sufficient agreement with experimental results within model limitations. The extensions in CAABA/MECCA are transferred to the 3D atmospheric model MESSy for a comprehensive evaluation of the impact of aqueous- and/or aerosol-phase chemistry on SOA at a global scale in a follow-up study.\u0000","PeriodicalId":12799,"journal":{"name":"Geoscientific Model Development","volume":null,"pages":null},"PeriodicalIF":5.1,"publicationDate":"2024-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141099316","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-24DOI: 10.5194/gmd-17-4229-2024
David Sandoval, I. Prentice, Rodolfo L. B. Nóbrega
Abstract. The current representation of key processes in land surface models (LSMs) for estimating water and energy balances still relies heavily on empirical equations that require calibration oriented to site-specific characteristics. When multiple parameters are used, different combinations of parameter values can produce equally acceptable results, leading to a risk of obtaining “the right answers for the wrong reasons”, compromising the reproducibility of the simulations and limiting the ecological interpretability of the results. To address this problem and reduce the need for free parameters, here we present novel formulations based on first principles to calculate key components of water and energy balances, extending the already parsimonious SPLASH model v.1.0 (Davis et al., 2017, GMD). We found analytical solutions for many processes, enabling us to increase spatial resolution and include the terrain effects directly in the calculations without unreasonably inflating computational demands. This calibration-free model estimates quantities such as net radiation, evapotranspiration, condensation, soil water content, surface runoff, subsurface lateral flow, and snow-water equivalent. These quantities are derived from readily available meteorological data such as near-surface air temperature, precipitation, and solar radiation, as well as soil physical properties. Whenever empirical formulations were required, e.g., pedotransfer functions and albedo–snow cover relationships, we selected and optimized the best-performing equations through a combination of remote sensing and globally distributed terrestrial observational datasets. Simulations at global scales at different resolutions were run to evaluate spatial patterns, while simulations with point-based observations were run to evaluate seasonal patterns using data from hundreds of stations and comparisons with the VIC-3L model, demonstrating improved performance based on statistical tests and observational comparisons. In summary, our model offers a more robust, reproducible, and ecologically interpretable solution compared to more complex LSMs.
{"title":"Simple process-led algorithms for simulating habitats (SPLASH v.2.0): robust calculations of water and energy fluxes","authors":"David Sandoval, I. Prentice, Rodolfo L. B. Nóbrega","doi":"10.5194/gmd-17-4229-2024","DOIUrl":"https://doi.org/10.5194/gmd-17-4229-2024","url":null,"abstract":"Abstract. The current representation of key processes in land surface models (LSMs) for estimating water and energy balances still relies heavily on empirical equations that require calibration oriented to site-specific characteristics. When multiple parameters are used, different combinations of parameter values can produce equally acceptable results, leading to a risk of obtaining “the right answers for the wrong reasons”, compromising the reproducibility of the simulations and limiting the ecological interpretability of the results. To address this problem and reduce the need for free parameters, here we present novel formulations based on first principles to calculate key components of water and energy balances, extending the already parsimonious SPLASH model v.1.0 (Davis et al., 2017, GMD). We found analytical solutions for many processes, enabling us to increase spatial resolution and include the terrain effects directly in the calculations without unreasonably inflating computational demands. This calibration-free model estimates quantities such as net radiation, evapotranspiration, condensation, soil water content, surface runoff, subsurface lateral flow, and snow-water equivalent. These quantities are derived from readily available meteorological data such as near-surface air temperature, precipitation, and solar radiation, as well as soil physical properties. Whenever empirical formulations were required, e.g., pedotransfer functions and albedo–snow cover relationships, we selected and optimized the best-performing equations through a combination of remote sensing and globally distributed terrestrial observational datasets. Simulations at global scales at different resolutions were run to evaluate spatial patterns, while simulations with point-based observations were run to evaluate seasonal patterns using data from hundreds of stations and comparisons with the VIC-3L model, demonstrating improved performance based on statistical tests and observational comparisons. In summary, our model offers a more robust, reproducible, and ecologically interpretable solution compared to more complex LSMs.\u0000","PeriodicalId":12799,"journal":{"name":"Geoscientific Model Development","volume":null,"pages":null},"PeriodicalIF":5.1,"publicationDate":"2024-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141101838","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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