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At least 487 records · Page 27

Agricultural Breadbaskets Shift Poleward Given Adaptive Farmer Behavior Under Climate Change

Modern food production is spatially concentrated in global “breadbaskets”. A major unresolved question is whether these peak production regions will shift poleward as the climate warms, allowing some recovery of potential climaterelated losses. While agricultural impacts studies to date have focused on currently cultivated land, the Global Gridded Crop Model Intercomparison Project (GGCMI) Phase 2 experiment allows us to assess changes in both yields and the location of peak productivity regions under warming. We examine crop responses under projected end-of-century warming using 7 process-based models simulating 5 major crops (maize, rice, soybeans, and spring and winter wheat) with a variety of adaptation strategies. We find that in no-adaptation cases, when planting date and cultivar choices are held fixed, regions of peak production remain stationary and yield losses can be severe, since growing seasons contract strongly with warming. When adaptations in management practices are allowed (cultivars that retain growing season length under warming and modified planting dates), peak productivity zones shift poleward and yield losses are largely recovered. While most growing-zone shifts are ultimately limited by geography, breadbaskets studied here move poleward over 600 km on average by end of the century under RCP8.5. These results suggest that agricultural impacts assessments can be strongly biased if restricted in spatial area or in the scope of adaptive behavior considered. Accurate evaluation of food security under climate change requires global modeling and careful treatment of adaptation strategies.

adaptation↗

Spatiotemporal Super-Resolution with Generative Machine Learning for Creating Renewable Energy Resource Data Under Climate Change Scenarios

As we plan for a future with higher penetrations of renewables and increasing electrification, it becomes more important to understand how the electricity grid will operate under a variety of weather events. We must also consider that the weather our future grid will experience will be different and possibly more extreme than the historical weather that we have extensive data for. We can use data from global climate models (GCMs) to help understand how our climate may change over the next several decades, but there is often a significant gap between the low-resolution GCM data and the high-resolution weather data required to study power systems under specific weather events. Therefore, our objective in this work is to develop tools that can bridge this gap by using low-resolution GCM data to create realistic high-resolution weather datasets that can be used to study renewable energy generation and electricity demand. To accomplish this objective, we have developed a set of generative machine learning models that can rapidly downscale GCM daily average output data at an approximate grid resolution of 100km to hourly data at an approximate 4 km grid resolution. The models can be used to create high resolution data from nearly any GCM included in the Coupled Model Intercomparison Project (CMIP) Phase 5 or 6. Our methods include all datasets regularly used to study the integration of wind and solar power plants as well as changes in electricity demand due to heating and cooling loads. These models and datasets enable power systems modelers to study climate change-influenced weather events and their impact on the grid. We have downscaled and validated wind, solar, temperature, and humidity data with very promising results. The generative machine learning methods are computationally efficient and produce data that has similar statistical characteristics to current state-of-the-art historical datasets. We have trained initial generative models and produced an initial dataset collectively referred to as Sup3rCC: Super-Resolved Renewable Energy Resource Data with Climate Change Impacts. The data covers a (mostly) historical period from 2015-2025 and a future period from 2050-2059. We have also taken hypothetical high-electrification load data and scaled the heating and cooling loads with respect to the 2050-2059 high-resolution Sup3rCC meteorology. The results show how future levels of renewable energy generation and electrified load may be impacted by climate change, setting the stage for capacity expansion models to consider a dynamic climate through model years.

climate change↗

Inhomogeneous Forcing and Transient Climate Sensitivity

Understanding climate sensitivity is critical to projecting climate change in response to a given forcing scenario. Recent analyses have suggested that transient climate sensitivity is at the low end of the present model range taking into account the reduced warming rates during the past 10-15 years during which forcing has increased markedly. In contrast, comparisons of modelled feedback processes with observations indicate that the most realistic models have higher sensitivities. Here I analyse results from recent climate modelling intercomparison projects to demonstrate that transient climate sensitivity to historical aerosols and ozone is substantially greater than the transient climate sensitivity to CO2. This enhanced sensitivity is primarily caused by more of the forcing being located at Northern Hemisphere middle to high latitudes where it triggers more rapid land responses and stronger feedbacks. I find that accounting for this enhancement largely reconciles the two sets of results, and I conclude that the lowest end of the range of transient climate response to CO2 in present models and assessments (less than 1.3 C) is very unlikely.

sensitivity↗

The Spread of Ocean Heat Uptake Efficiency Traced to Ocean Salinity

Abstract Ocean heat uptake (OHU) plays an important role in determining the rate of surface warming under CO 2 forcing. We examined the transient response to CO 2 forcing in a set of 30 climate models from Coupled Model Intercomparison Project Phase 6 (CMIP6) and found that the intermodel spread in OHU efficiency largely depends on the base‐state ocean stratification, especially in the Southern Ocean. This dependence is primarily traced to ocean salinity rather than ocean temperature. Models with weaker ocean stratification primarily due to higher upper‐ocean salinity tend to sequester heat into the deeper ocean, leading to a lower rate of surface warming, a primary reason for higher OHU efficiency; the stratification impact on total OHU is secondary. We applied the relationship between salinity and OHU efficiency for an emergent constraint on OHU efficiency, suggesting an OHU efficiency higher than the CMIP6 multimodel average and arguing against models with extremely low efficiency.

54 ENVIRONMENTAL SCIENCES↗

Across‐model spread and shrinking in predicting peatland carbon dynamics under global change

Abstract Large across‐model spread in simulating land carbon (C) dynamics has been ubiquitously demonstrated in model intercomparison projects (MIPs), and became a major impediment in advancing climate change prediction. Thus, it is imperative to identify underlying sources of the spread. Here, we used a novel matrix approach to analytically pin down the sources of across‐model spread in transient peatland C dynamics in response to a factorial combination of two atmospheric CO 2 levels and five temperature levels. We developed a matrix‐based MIP by converting the C cycle module of eight land models (i.e., TEM, CENTURY4, DALEC2, TECO, FBDC, CASA, CLM4.5 and ORCHIDEE) into eight matrix models. While the model average of ecosystem C storage was comparable to the measurement, the simulation differed largely among models, mainly due to inter‐model difference in baseline C residence time. Models generally overestimated net ecosystem production (NEP), with a large spread that was mainly attributed to inter‐model difference in environmental scalar. Based on the sources of spreads identified, we sequentially standardized model parameters to shrink simulated ecosystem C storage and NEP to almost none. Models generally captured the observed negative response of NEP to warming, but differed largely in the magnitude of response, due to differences in baseline C residence time and temperature sensitivity of decomposition. While there was a lack of response of NEP to elevated CO 2 (eCO 2 ) concentrations in the measurements, simulated NEP responded positively to eCO 2 concentrations in most models, due to the positive responses of simulated net primary production. Our study used one case study in Minnesota peatland to demonstrate that the sources of across‐model spreads in simulating transient C dynamics can be precisely traced to model structures and parameters, regardless of their complexity, given the protocol that all the matrix models were driven by the same gross primary production and environmental variables.

Hou, Enqing↗

A Parameterization of Sub-grid Topographical Effects on Solar Radiation in the E3SM Land Model (Version 1.0): Implementation and Evaluation Over the Tibetan Plateau

Topography exerts significant influences on the incoming solar radiation at the land surface. A few stand-alone regional and global atmospheric models have included parameterizations for sub-grid topographic effects on solar radiation. However, nearly all Earth System Models (ESMs) that participated in the Coupled Model Intercomparison Project (CMIP6) use a plane-parallel (PP) radiative transfer scheme that assumes the terrain is flat. In this study, we incorporated a well-validated sub-grid topographic (TOP) parameterization in the Energy Exascale Earth System Model (E3SM) Land Model (ELM) version 1.0 to quantify the effects of sub-grid topography on solar radiation flux, including the shadow effects and multi-scattering between adjacent terrain. The Moderate Resolution Imaging Spectroradiometer (MODIS) data was used to evaluate the performance of ELM. We studied the role of sub-grid topography by performing ELM simulations with the PP and TOP schemes over the Tibetan Plateau (TP). Additional ELM simulations were performed at multiple spatial resolutions to investigate the role of spatial scale on sub-grid topographic effects on solar radiation. When compared to MODIS data, incorporating the sub-grid topographic effects overall reduces the biases of ELM in simulating surface energy balance, snow cover and surface temperature especially in the high-elevation and snow-cover regions over the TP. Topography has non-negligible effects on surface energy budget, snow cover, and surface temperature over the TP. The absolute differences in surface energy fluxes for net solar radiation, latent heat flux, and sensible heat flux between TOP and PP exceed 20 W/m 2 , 10 W/m 2 , and 5 W/m 2 , respectively. The differences in land surface albedo, snow cover fraction, and surface temperature between TOP and PP exceed 0.1, 20%, and 1K, respectively. The magnitude of the sub-grid topographic effects is dependent on seasons and elevations, and is also sensitive to the spatial scales. Although the sub-grid topographic effects on solar radiation are more significant with more spatial details at finer spatial scales, they cannot be simply neglected at coarse spatial scales. The inclusion of sub-grid topographic effects on solar radiation parameterization in ELM will contribute to advancing our understanding of the role of the surface topography on terrestrial processes over complex terrain.

58 GEOSCIENCES↗

Analysis of Present Day and Future OH and Methane Lifetime in the ACCMIP Simulations

Results from simulations performed for the Atmospheric Chemistry and Climate Modeling Intercomparison Project (ACCMIP) are analysed to examine how OH and methane lifetime may change from present day to the future, under different climate and emissions scenarios. Present day (2000) mean tropospheric chemical lifetime derived from the ACCMIP multi-model mean is 9.8+/-1.6 yr (9.3+/-0.9 yr when only including selected models), lower than a recent observationally-based estimate, but with a similar range to previous multi-model estimates. Future model projections are based on the four Representative Concentration Pathways (RCPs), and the results also exhibit a large range. Decreases in global methane lifetime of 4.5 +/- 9.1% are simulated for the scenario with lowest radiative forcing by 2100 (RCP 2.6), while increases of 8.5+/-10.4% are simulated for the scenario with highest radiative forcing (RCP 8.5). In this scenario, the key driver of the evolution of OH and methane lifetime is methane itself, since its concentration more than doubles by 2100 and it consumes much of the OH that exists in the troposphere. Stratospheric ozone recovery, which drives tropospheric OH decreases through photolysis modifications, also plays a partial role. In the other scenarios, where methane changes are less drastic, the interplay between various competing drivers leads to smaller and more diverse OH and methane lifetime responses, which are difficult to attribute. For all scenarios, regional OH changes are even more variable, with the most robust feature being the large decreases over the remote oceans in RCP8.5. Through a regression analysis, we suggest that differences in emissions of non-methane volatile organic compounds and in the simulation of photolysis rates may be the main factors causing the differences in simulated present day OH and methane lifetime. Diversity in predicted changes between present day and future OH was found to be associated more strongly with differences in modelled temperature and stratospheric ozone changes. Finally, through perturbation experiments we calculated an OH feedback factor (F) of 1.24 from present day conditions (1.50 from 2100 RCP8.5 conditions) and a climate feedback on methane lifetime of 0.33+-0.13 yr/K, on average. Models that did not include interactive stratospheric ozone effects on photolysis showed a stronger sensitivity to climate, as they did not account for negative effects of climate-driven stratospheric ozone recovery on tropospheric OH, which would have partly offset the overall OH/methane lifetime response to climate change.

atmospheric composition↗

Global Corn Heat Stress: Mean and SD of Degree Days Above 29°C based on NEX-GDDP-CMIP6 Climate Projections

Description This global dataset provides the estimated mean and standard deviation (SD) of corn heat stress (degree days above 29°C) for a set of climate models in NEX-GDDP-CMIP6 at 0.25-degree resolution. The NEX-GDDP-CMIP6 dataset is comprised of global downscaled climate scenarios derived from the General Circulation Model (GCM) runs conducted under the Coupled Model Intercomparison Project Phase 6 (CMIP6). The current dataset includes: Long-Term Average Degree Days Above 29°C- Historical Long-Term Average Degree Days Above 29°C- SSP245 Long-Term Standard Deviation of Degree Days Above 29°C- Historical Long-Term Standard Deviation of Degree Days Above 29°C- SSP245 The mean and SD are calculated over 1985-2014 for the historical period and over 2035-2064 for future projections. A full description of methods, including growing season, daily temperature distribution, and statistical coefficients, can be found in Haqiqi (2024). The source climate data are obtained from https://ds.nccs.nasa.gov/thredds2/catalog/catalog.html and are described in Thrasher et al (2022). The codes used to create this dataset are available at https://github.com/ihaqiqi/dd29c_nex_cmip6. Acknowledgments This work was supported by the US Department of Energy, Office of Science, Biological and Environmental Research Program, Earth and Environmental Systems Modeling, MultiSector Dynamics under Cooperative Agreement DE-SC0022141. The data processing, computation, and storage were completed on Purdue Anvil supercomputer and cyberinfrastructure supported by the National Science Foundation HDR award # 2118329: "NSF Institute for Geospatial Understanding through an Integrative Discovery Environment (I-GUIDE)". References Haqiqi. I. (2024). Trade can buffer climate-induced risks and volatilities in crop supply. Environmental Research: Food Systems. https://doi.org/10.1088/2976-601X/ad7d12 Thrasher, B., Wang, W., Michaelis, A., Melton, F., Lee, T., & Nemani, R. (2022). NASA global daily downscaled projections, CMIP6. Scientific Data, 9(1), 262. https://doi.org/10.1038/s41597-022-01393-4

Climate Change↗

The effects of anthropogenic and volcanic aerosols and greenhouse gases on twentieth century Sahel precipitation

Abstract There is little scientific consensus on the importance of external climate forcings—including anthropogenic aerosols, volcanic aerosols, and greenhouse gases (GHG)—relative to each other and to internal variability in dictating past and future Sahel rainfall. We address this query by relating a 3-tiered multi-model mean (MMM) over the Climate Model Intercomparison Project phase 5 “twentieth century” and pre-Industrial control simulations to observations. The comparison of single-forcing and historical simulations highlights the importance of anthropogenic and volcanic aerosols over GHG in generating forced Sahel rainfall variability in models. However, the forced MMM only accounts for a small fraction of observed variance. A residual consistency test shows that simulated internal variability cannot explain the residual observed multidecadal variability, and points to model deficiency in simulating multidecadal variability in the forced response, internal variability, or both.

54 ENVIRONMENTAL SCIENCES↗

A parameterization of sub-grid topographical effects on solar radiation in the E3SM Land Model (version 1.0): implementation and evaluation over the Tibetan Plateau

Abstract. Topography exerts significant influences on the incoming solar radiation at the land surface. A few stand-alone regional and global atmospheric models have included parameterizations for sub-grid topographic effects on solar radiation. However, nearly all Earth system models (ESMs) that participated in the Coupled Model Intercomparison Project (CMIP6) use a plane-parallel (PP) radiative transfer scheme that assumes that the terrain is flat. In this study, we incorporated a well-validated sub-grid topographic (TOP) parameterization in the Energy Exascale Earth System Model (E3SM) Land Model (ELM) version 1.0 to quantify the effects of sub-grid topography on solar radiation flux, including the shadow effects and multi-scattering between adjacent terrain. We studied the role of sub-grid topography by performing ELM simulations with the PP and TOP schemes over the Tibetan Plateau (TP). Additional ELM simulations were performed at multiple spatial resolutions to investigate the role of spatial scale on sub-grid topographic effects on solar radiation. The Moderate Resolution Imaging Spectroradiometer (MODIS) data was used to compare with the ELM simulations. The results show that topography has non-negligible effects on surface energy budget, snow cover, snow depth, and surface temperature over the TP. The absolute differences in surface energy fluxes for net solar radiation, latent heat flux, and sensible heat flux between TOP and PP exceed 20, 10, and 5 W m−2, respectively. The differences in land surface albedo, snow cover fraction, snow depth, and surface temperature between TOP and PP exceed 0.1, 0.1, 10 cm, and 1 K, respectively. The magnitude of the sub-grid topographic effects is dependent on seasons and elevations and is also sensitive to the spatial scales. Although the sub-grid topographic effects on solar radiation are larger with more spatial details at finer spatial scales, they cannot be simply neglected at coarse spatial scales. When compared to MODIS data, incorporating the sub-grid topographic effects overall reduces the biases of ELM in simulating surface energy balance, snow cover, and surface temperature, especially in the high-elevation and snow-covered regions over the TP. The inclusion of sub-grid topographic effects on solar radiation parameterization in ELM will contribute to advancing our understanding of the role of the surface topography on terrestrial processes over complex terrain.

Hao, Dalei (ORCID:0000000334979774)↗

Machine Learning Emulators and Empirical Models Combining Climate and Global Crop Models for Seasonal Agricultural Production

We present results from several connected efforts to apply machine learning methods to estimates of seasonal agricultural production anomalies around the world. First, we apply the XGBoost Random Forest method to fit emulators that mimic global crop models participating in the Agricultural Model Intercomparison and Improvement Project (AgMIP) Global Gridded Crop Model Intercomparison (GGCMI). These are the same models used in the agricultural sector simulations of the Inter-Sectoral Impacts Model Intercomparison Project (ISIMIP). These emulators use 8 climate variables split across 5 sub-seasonal representations of the growing season for each ½ degree grid cell around the world for maize, wheat, rice and soybeans. Emulators are useful for estimating conditions that have not already been simulated by GGCMI (e.g., in a seasonal prediction model) and also to diagnose model differences and capabilities. For example, emulators of the pDSSAT maize model tend to be more reliant on mean temperatures than the LPJmL model, and few models have strong responses to cold extremes. Second, we use a similar XGBoost approach to fit empirical models for national production data for the top 20 producing countries according to the United Nations Food and Agricultural Organization (FAO). Models utilize both climate observations and the GGCM models as predictors, resulting in skillful models for many (but not all) top producing-countries. The patterns of climate and crop model features selected indicate regions and systems that are better or worse simulated by the GGCMs. For example, information in cold extreme predictors is often combined with GGCM output predictors to provide sensitivity that models may underrepresent.

machine learning↗

Influences of North Pacific Ocean Domain Extent on the Western U.S. Winter Hydroclimatology in Variable-Resolution CESM

Variable-resolution global climate models (VRGCMs) are a dynamical downscaling method that can reach spatiotemporal scales needed for regional climate assessments. Over the years, several users of VRGCMs have assumed where the location and extent of the refinement domain should be based on knowledge of the prevailing storm tracks and resolution dependence of important regional climate processes (e.g., atmospheric rivers [ARs] and orographic uplift), but the effect of high-resolution domain size and extent on the simulation of downstream hydroclimatic phenomena has not been systematically evaluated. Here, we use variable resolution in the Community Earth System Model (VR-CESM) to perform such a test. To do this, three VR-CESM grids were generated that span the entire, two thirds, and one third of the North Pacific and evaluated for a 30-year climatology using Atmospheric Model Intercomparison Project protocols. Simulations are compared with reanalysis products offshore (fifth-generation of the European Centre for Medium-Range Weather Forecasts atmospheric reanalysis [ERA5]) and onshore (Livneh, 2015, https://doi.org/10.1038/sdata.2015.42, and Parameter-elevation Regressions on Independent Slopes Model [PRISM]) of the western United States. The westward expansion of refinement domain influenced integrated vapor transport (IVT), which was generally high biased but minimally impacted AR characteristics. Due to slight differences in landfalling AR counts in the western United States, California winter precipitation generally improved with westward expansion of the refinement domain. Western U.S. mountain snowpack and surface temperatures were insensitive to refinement domain size and were more influenced by changes in topographic resolution and/or land surface model version. Here, given minimal dependence of simulated western U.S. hydroclimate on refinement domain size over the North Pacific, we advise future VR-CESM studies to focus grid resolution on better resolving land surface heterogeneity.

54 ENVIRONMENTAL SCIENCES↗

Increased Variability of Biomass Burning Emissions in CMIP6 Amplifies Hydrologic Cycle in the CESM2 Large Ensemble

Abstract Historical simulations performed for the Coupled Model Intercomparison Project Phase 6 used biomass burning emissions between 1997 and 2014 containing higher spatial and temporal variability compared to emission inventories specified for earlier years, and compared to emissions used in previous (e.g., CMIP5) simulation intercomparisons. Using the Community Earth System Model version 2 Large Ensemble, we show this increased biomass burning emissions variability leads to amplification of the hydrologic cycle poleward of 40°N. Notably, the high variability of biomass burning emissions leads to increased latent heat fluxes, column‐integrated precipitable water, and precipitation. Greater ocean heat uptake, weaker meridional energy transport from the tropics, greater atmospheric shortwave and longwave absorption, and lower relative humidity act to moderate this hydrologic cycle amplification. Our results suggest it is not only the secular changes (on multidecadal timescales) in biomass burning emissions that impact the hydrologic cycle, but also the shorter timescale variability in emissions.

Heyblom, Kyle B.↗

The Changing Influence of Precipitation on Soil Moisture Drought With Warming in the Mediterranean and Western North America

Abstract Anthropogenic climate change has already affected drought severity and risk across many regions, and climate models project additional increases in drought risk with future warming. Historically, droughts are typically caused by periods of below‐normal precipitation and terminated by average or above‐normal precipitation. In many regions, however, soil moisture is projected to decrease primarily through warming‐driven increases in evaporative demand, potentially affecting the ability of negative precipitation anomalies to cause drought and positive precipitation anomalies to terminate drought. Here, we use climate model simulations from Phase Six of the Coupled Model Intercomparison Project (CMIP6) to investigate how different levels of warming (1, 2, and 3°C) affect the influence of precipitation on soil moisture drought in the Mediterranean and Western North America regions. We demonstrate that the same monthly precipitation deficits (25th percentile relative to a preindustrial baseline) at a global warming level of 2°C increase the probability of both surface and rootzone soil moisture drought by 29% in the Mediterranean and 32% and 6% in Western North America compared to the preindustrial baseline. Furthermore, the probability of a dry (25th percentile relative to a preindustrial baseline) surface soil moisture month given a high (75th percentile relative to a preindustrial baseline) precipitation month is 6 (Mediterranean) and 3 (Western North America) times more likely in a 2°C world compared to the preindustrial baseline. For these regions, warming will likely increase the risk of soil moisture drought during low precipitation periods while simultaneously reducing the efficacy of high precipitation periods to terminate droughts.

Environmental Sciences & Ecology↗

Forecasted Climate Penalties to Surface Ozone Across the 21st Century

Future climate change may bring local benefits or penalties to surface ozone pollution, resulting from changing temperature, precipitation, and transport patterns, as well as climate-sensitive natural precursor emissions. Here, we estimate climate penalty and benefits at the end of this century using version 2.0 of the Global Change and Air Pollution (GCAP 2.0) model framework, a one-way offline coupling between version E2.1 of the NASA Goddard Institute for Space Studies (GISS) general circulation model (GCM) and the GEOS-Chem global 3-D chemical-transport model (CTM). We archive meteorology for driving GEOS-Chem from the E2.1 contributions to Phase 6 of the Coupled Model Intercomparison Project (CMIP6) for the present day (2005-2014 C.E.) and end-of-the 21st century (2090-2099 C.E.). Emissions and boundary conditions used for input to GEOS-Chem are consistent with the CMIP6 experimental design. We apply the model to isolate the impact of forecasted anthropogenic precursor emission changes versus climate-only driven changes on surface ozone for warming limited to 1.9 W m-2 versus 8.5 W m-2 above preindustrial levels. We find a global climate penalty of 47 (16-77) thousand additional deaths due to surface ozone at the end of the century in the extreme warming scenario, but only 13 (4-21) thousand additional deaths in the extreme mitigation scenario. Similarly, the extreme warming scenario is associated with a 40 % increase in economic losses due to crop damage from climate-only changes in surface ozone, but only an 8 % increase in the extreme mitigation scenario. The simulated climate penalties to surface ozone are most severe in South and East Asia.

Climate penalties↗

Wetter California Projected by CMIP6 Models With Observational Constraints Under a High GHG Emission Scenario

As the world's fifth-largest economy entity, California (CA) is vulnerable to climate changes especially the magnitude of winter (wet season) precipitation that is closely linked to regional drought severity, vegetation growth, and wildfire activities. However, the fate of CA precipitation in the future remains highly uncertain, for example, the state-of-the-art Earth system models (ESMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6) projected –3% to +42% and –27% to +63% precipitation changes in northern and central-southern CA, respectively, under the high greenhouse gas (GHG) emission scenario (SSP585). In this work, we applied the Pareto optimality concept and used observed teleconnection patterns (causal relationships between ocean regions and CA precipitation) to mechanistically constrain CMIP6 projected CA precipitations. We estimated that precipitation will robustly increase by 0.4–1.3 mm d –1 (10–34%) over northern CA and increase by 0.1–0.5 mm d –1 (7–32%) over central-southern CA by the end of the 21st century compared with present-day. Up to 71% of ESM projection uncertainties were reduced mainly due to the strong and consistent causal relationship between North American west coast sea level pressure and CA precipitation in both observations and CMIP6 models. Our results suggest that teleconnection patterns are powerful mechanistic constraints that can help explain and reduce uncertainties in ESM projections.

54 ENVIRONMENTAL SCIENCES↗

Quantitative evaluations of subtropical westerly jet simulations over East Asia based on multiple CMIP5 and CMIP6 GCMs

As a salient feature of the Asian monsoon system, the East Asian subtropical westerly jet (EASWJ) exerts significant impacts on weather and climate changes in China and even throughout East Asia. In this paper, we applied a new self-adaptive algorithm to detect the EASWJ, identify its boundaries, and then represent its characteristics by defining three indices: the intensity index, meridional displacement index, and width index. Compared to the reanalysis data, we carried out a comprehensive, objective, and quantitative EASWJ evaluation using historical experiments from multiple global climate models (GCMs) in the Coupled Model Intercomparison Project Phases 5 and 6 (CMIP5 and CMIP6). The results show that the multimodel ensemble mean (MME) of both CMIP5 and CMIP6 can simulate the characteristics of winter EASWJ well. While for the other three seasons, the MME of both phase models underestimate the 200-hPa zonal wind (U200) strength in the jet coverage area and overestimate the U200 outside the jet area, such simulation results weaken the meridional shear of the wind field. The EASWJ simulations from the CMIP5 GCMs had no consistent intensity or location characteristic tendencies, and most CMIP5 GCMs tended to simulate relatively wide-coverage jets. In contrast, most CMIP6 GCMs are inclined to simulate significantly weaker, wider, and more-northward jets. Compared to the predecessors in CMIP5, about half of CMIP6 GCMs significantly minimized the jet intensity bias, but remarkable errors were still observed in their jet location and coverage representations. Furthermore, the comparative analysis performed by classifying models based on their evaluated simulation results suggested that the simulated performance of the meridional temperature gradient was important for capturing the EASWJ characteristics. Further in-depth study of the causes of model differences is warranted to improve simulation results.

54 ENVIRONMENTAL SCIENCES↗

Validation and Sensitivity Analysis of a 1-D Lake Model Across Global Lakes

Lakes have important influence on weather and climate from local to global scales. However, their prediction using numerical models is notoriously difficult because lakes are highly heterogeneous across the globe, but observations are sparse. In this study, we assessed the performance of a 1-D lake model in simulating the thermal structures of 58 lakes with diverse morphometric and geographic characteristics by following the phase 2a local lake protocol of the Inter-sectoral Impact Model Intercomparison Project (ISIMIP2a). After calibration, the root-mean-square errors (RMSE) were below 2 °C for 70% and 75% of the lakes for epilimnion and full-profile temperature simulations, with an average of 1.71 °C and 1.43 °C, respectively. The model performance mainly depended on lake shape rather than location, supporting the possibility of grouping model parameters by lake shape for global applications. Furthermore, through machine-learning based parameter sensitivity tests, we identified turbulent heat fluxes, wind-driven mixing and water transparency as the major processes controlling lake thermal and mixing regimes. Snow density was also important for modeling the ice phenology of high-latitude lakes. The relative influence of the key processes and the corresponding parameters mainly depended on lake latitude and depth. Turbulent heat fluxes showed a decreasing importance in affecting epilimnion temperature with increasing latitude. Wind-driven mixing was less influential to lake stratification for deeper lakes while the impact of light extinction, on the contrary, showed a positive correlation with depth. Our findings may guide improvements in 1-D lake model parameterizations to achieve higher fidelity in simulating global lake thermal dynamics.

54 ENVIRONMENTAL SCIENCES↗