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At least 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
NASA NTRS2016

Uncertainty Assessment of the NASA Earth Exchange Global Daily Downscaled Climate Projections (NEX-GDDP) Dataset

The NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) dataset is comprised of downscaled climate projections that are derived from 21 General Circulation Model (GCM) runs conducted under the Coupled Model Intercomparison Project Phase 5 (CMIP5) and across two of the four greenhouse gas emissions scenarios (RCP4.5 and RCP8.5). Each of the climate projections includes daily maximum temperature, minimum temperature, and precipitation for the periods from 1950 through 2100 and the spatial resolution is 0.25 degrees (approximately 25 km by 25 km). The GDDP dataset has received warm welcome from the science community in conducting studies of climate change impacts at local to regional scales, but a comprehensive evaluation of its uncertainties is still missing. In this study, we apply the Perfect Model Experiment framework (Dixon et al. 2016) to quantify the key sources of uncertainties from the observational baseline dataset, the downscaling algorithm, and some intrinsic assumptions (e.g., the stationary assumption) inherent to the statistical downscaling techniques. We developed a set of metrics to evaluate downscaling errors resulted from bias-correction ("quantile-mapping"), spatial disaggregation, as well as the temporal-spatial non-stationarity of climate variability. Our results highlight the spatial disaggregation (or interpolation) errors, which dominate the overall uncertainties of the GDDP dataset, especially over heterogeneous and complex terrains (e.g., mountains and coastal area). In comparison, the temporal errors in the GDDP dataset tend to be more constrained. Our results also indicate that the downscaled daily precipitation also has relatively larger uncertainties than the temperature fields, reflecting the rather stochastic nature of precipitation in space. Therefore, our results provide insights in improving statistical downscaling algorithms and products in the future.

general circulation model (GCM)↗
DOE OSTI2024

CMIP6-based Multi-model Hydroclimate Projection over the Conterminous US, Version 1.1

This dataset presents a suite of high-resolution downscaled hydro-climate projections over the conterminous United States (CONUS) based on multiple selected Global Climate Models (GCMs) from the Coupled Models Intercomparison Project phase 6 (CMIP6). The CMIP6 GCMs are downscaled using either statistical (DBCCA) or dynamical (RegCM) downscaling approaches based on two meteorological reference datasets (Daymet and Livneh). Subsequently, the downscaled precipitation, temperature, and wind speed are employed to drive two calibrated hydrologic models (VIC and PRMS), enabling the simulation of projected future hydrologic responses across the CONUS. Each ensemble member covers the 1980-2019 baseline and 2020-2059 near-future periods under the high-end (SSP585) emission scenario. Moreover, utilizing only DBCCA and Daymet, the projections are further extended to the 2060-2099 far-future period and across three additional emission scenarios (SSP370, SSP245, and SSP126). This dataset is formulated to support the SECURE Water Act Section 9505 Assessment for the US Department of Energy (DOE) Water Power Technologies Office (WPTO). For further details on this dataset, please refer to Kao et al. (2022) and Rastogi et al. (2022).

13 HYDRO ENERGY↗
DOE OSTI2024

A multi-model assessment of the early last deglaciation (PMIP4 LDv1): a meltwater perspective

Abstract. The last deglaciation (∼20–11 ka BP) is a period of a major, long-term climate transition from a glacial to interglacial state that features multiple centennial- to decadal-scale abrupt climate variations whose root cause is still not fully understood. To better understand this time period, the Paleoclimate Modelling Intercomparison Project (PMIP) has provided a framework for an internationally coordinated endeavour in simulating the last deglaciation whilst encompassing a broad range of models. Here, we present a multi-model intercomparison of 17 transient simulations of the early part of the last deglaciation (∼20–15 ka BP) from nine different climate models spanning a range of model complexities and uncertain boundary conditions and forcings. The numerous simulations available provide the opportunity to better understand the chain of events and mechanisms of climate changes between 20 and 15 ka BP and our collective ability to simulate them. We conclude that the amount of freshwater forcing and whether it follows the ice sheet reconstruction or induces an inferred Atlantic meridional overturning circulation (AMOC) history, heavily impacts the deglacial climate evolution for each simulation rather than differences in the model physics. The course of the deglaciation is consistent between simulations except when the freshwater forcing is above 0.1 Sv – at least 70 % of the simulations agree that there is warming by 15 ka BP in most places excluding the location of meltwater input. For simulations with freshwater forcings that exceed 0.1 Sv from 18 ka BP, warming is delayed in the North Atlantic and surface air temperature correlations with AMOC strength are much higher. However, we find that the state of the AMOC coming out of the Last Glacial Maximum (LGM) also plays a key role in the AMOC sensitivity to model forcings. In addition, we show that the response of each model to the chosen meltwater scenario depends largely on the sensitivity of the model to the freshwater forcing and other aspects of the experimental design (e.g. CO2 forcing or ice sheet reconstruction). The results provide insight into the ability of our models to simulate the first part of the deglaciation and how choices between uncertain boundary conditions and forcings, with a focus on freshwater fluxes, can impact model outputs. We can use these findings as helpful insight in the design of future simulations of this time period.

Snoll, Brooke (ORCID:0000000230914394)↗
DOE OSTI2022

A framework for ensemble modelling of climate change impacts on lakes worldwide: the ISIMIP Lake Sector

Abstract. Empirical evidence demonstrates that lakes and reservoirs are warming across the globe. Consequently, there is an increased need to project future changes in lake thermal structure and resulting changes in lake biogeochemistry in order to plan for the likely impacts. Previous studies of the impacts of climate change on lakes have often relied on a single model forced with limited scenario-driven projections of future climate for a relatively small number of lakes. As a result, our understanding of the effects of climate change on lakes is fragmentary, based on scattered studies using different data sources and modelling protocols, and mainly focused on individual lakes or lake regions. This has precluded identification of the main impacts of climate change on lakes at global and regional scales and has likely contributed to the lack of lake water quality considerations in policy-relevant documents, such as the Assessment Reports of the Intergovernmental Panel on Climate Change (IPCC). Here, we describe a simulation protocol developed by the Lake Sector of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) for simulating climate change impacts on lakes using an ensemble of lake models and climate change scenarios for ISIMIP phases 2 and 3. The protocol prescribes lake simulations driven by climate forcing from gridded observations and different Earth system models under various representative greenhouse gas concentration pathways (RCPs), all consistently bias-corrected on a 0.5∘ × 0.5∘ global grid. In ISIMIP phase 2, 11 lake models were forced with these data to project the thermal structure of 62 well-studied lakes where data were available for calibration under historical conditions, and using uncalibrated models for 17 500 lakes defined for all global grid cells containing lakes. In ISIMIP phase 3, this approach was expanded to consider more lakes, more models, and more processes. The ISIMIP Lake Sector is the largest international effort to project future water temperature, thermal structure, and ice phenology of lakes at local and global scales and paves the way for future simulations of the impacts of climate change on water quality and biogeochemistry in lakes.

58 GEOSCIENCES↗
NASA NTRS2018

Evaluation of a High-Resolution Numerical Weather Prediction Model’s Simulated Clouds Using Observations from CloudSat, GOES-13 and in Situ Aircraft

This study aimed to assess tropical cloud properties predicted by Environment and Climate Change Canada’s Global Environmental Multiscale (GEM)modelwhen run with the Milbrandt–Yau double-moment cloud microphysical scheme and one-way nesting that culminated at a (∼300 km)2 inner domain with 0.25 km horizontal grid spacing. The assessment utilized satellite and in situ data collected during the High Ice Water Content (HIWC) and High Altitude Ice Crystals (HAIC) projects for a mesoscale convective system on 16May 2015 over French Guiana.Data fromCloud- Sat’s cloud-profiling radar and GOES-13’s imager were compared to data either simulated directly by GEM or produced by operating on GEM’s cloud data with both the CFMIP (Cloud Feedback Model Intercomparison Project) Observation Simulator Package (COSP) instrument simulator and a three-dimensional Monte Carlo solar radiative transfer model. In situ observations were made from research aircraft – Canada’s National Research Council Convair-580 and the French SAFIRE Falcon-20 – whose flight paths were aligned with CloudSat’s ground-track. Spatial and temporal shifts of clouds simulated by GEM compared well to GOES-13 imagery. There are, however, differences between simulated and observed amounts of high and low cloud. While GEM did well at predicting ranges of ice-water content (IWC) near 11 km altitude (Falcon-20), it produces too much graupel and snow near 7 km (Convair-580). This produced large differences between CloudSat’s and COSP-generated radar reflectivities and two-way attenuations. On the other hand, CloudSat’s inferred values of IWC agree well with in situ samples at both altitudes. Generally, GEM’s visible reflectances exceeded GOES-13’s on account of having produced too much low-level liquid cloud. It is expected that GEM’s disproportioning of cloud hydrometeors will improve once it includes a better representation of secondary ice production.

Qu, Zhipeng↗
DOE OSTI2021

Regional environmental controllers influence continental scale soil carbon stocks and future carbon dynamics

Abstract Understanding the influence of environmental factors on soil organic carbon (SOC) is critical for quantifying and reducing the uncertainty in carbon climate feedback projections under changing environmental conditions. We explored the effect of climatic variables, land cover types, topographic attributes, soil types and bedrock geology on SOC stocks of top 1 m depth across conterminous United States (US) ecoregions. Using 4559 soil profile observations and high-resolution data of environmental factors, we identified dominant environmental controllers of SOC stocks in 21 US ecoregions using geographically weighted regression. We used projected climatic data of SSP126 and SSP585 scenarios from GFDL-ESM 4 Earth System Model of Coupled Model Intercomparison Project phase 6 to predict SOC stock changes across continental US between 2030 and 2100. Both baseline and predicted changes in SOC stocks were compared with SOC stocks represented in GFDL-ESM4 projections. Among 56 environmental predictors, we found 12 as dominant controllers across all ecoregions. The adjusted geospatial model with the 12 environmental controllers showed an R 2 of 0.48 in testing dataset. Higher precipitation and lower temperatures were associated with higher levels of SOC stocks in majority of ecoregions. Changes in land cover types (vegetation properties) was important in drier ecosystem as North American deserts, whereas soil types and topography were more important in American prairies. Wetlands of the Everglades was highly sensitive to projected temperature changes. The SOC stocks did not change under SSP126 until 2100, however SOC stocks decreased up to 21% under SSP585. Our results, based on environmental controllers of SOC stocks, help to predict impacts of changing environmental conditions on SOC stocks more reliably and may reduce uncertainties found in both, geospatial and Earth System Models. In addition, the description of different environmental controllers for US ecoregions can help to describe the scope and importance of global and local models.

58 GEOSCIENCES↗
DOE OSTI2022

CMIP6-based Multi-model Hydroclimate Projection over the Conterminous US

We present a suite of high-resolution downscaled hydro-climate projections over the conterminous United States (CONUS) based on a six-member General Climate Model (GCM) ensemble from the Coupled Models Intercomparison Project phase 6 (CMIP6). The CMIP6 GCMs are downscaled using two different downscaling approaches (statistical-based DBCCA & dynamical-based RegCM) based on two meteorological reference observations (Daymet & Livneh), and then fed to two calibrated hydrologic models (VIC & PRMS) to simulate projected future hydrologic responses. Each ensemble member covers 1980–2019 in the historic period and 2020–2059 in the near-term future period under the high-end (SSP585) emission scenario. Major variables such as daily maximum temperature (tmax), daily minimum temperature (tmin), daily total precipitation (prcp), daily average wind speed (wind), and daily total runoff (runoff) at 1/24° (~4 km) spatial resolution across the CONUS are provided through this data portal. This dataset is derived to support the SECURE Water Act Section 9505 Assessment for the US Department of Energy (DOE) Water Power Technologies Office (WPTO). Further details of this dataset can be referred to Kao et al. (2022) and Rastogi et al. (2022).

13 HYDRO ENERGY↗
DOE OSTI2022

A Positive Low Cloud–Sea Surface Temperature Feedback in the East Asian Marginal Seas during El Niño Mature Winters and Their Following Spring

During the winter and subsequent spring of an El Niño year, the East Asian marginal sea (EAMS) exhibits positive sea surface temperature anomalies (SSTAs) and fewer low clouds, while the western North Pacific experiences negative SSTAs. In this study, we suggest that the positive SSTAs in EAMS are maintained by a positive low cloud–SST feedback. In neutral winters and springs, the EAMS is covered by low clouds, which have a cooling effect on surface temperatures. During an El Niño year, a western North Pacific anomalous anticyclone is established, and along its northwestern flank, there are favorable conditions for convergence of moisture and weaker surface latent heat flux over the EAMS. Once a positive SSTA has been established, a further reduction of turbulent mixing results in less low cloud and enhanced solar heating of the ocean mixed layer; this reinforces and maintains both the positive SSTA and the lack of low cloud via a positive feedback mechanism. Further, the concurrent increase of low cloud–SST feedback and anticyclone circulation strengths is evident in the coupled-model simulations from phase 6 of the Coupled Model Intercomparison Project. Furthermore, sensitivity experiments, performed with the atmospheric components of Community Earth System Model (CESM2), reveal that a positive SSTA helps to maintain the western North Pacific anomalous anticyclone. Four pacemaker-coupled experiments by CESM2, with sea surface temperature in the equatorial Pacific restored to the observational anomalies plus the model climatology and altered low cloud feedback over EAMS, suggest that the low cloud–SST feedback results in more than the maintenance of a positive SSTA over the EAMS: the positive feedback is also a previously overlooked mechanism for the maintenance of the western North Pacific anomalous anticyclone.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Subpolar North Atlantic Mean State Affects the Response of the Atlantic Meridional Overturning Circulation to the North Atlantic Oscillation in CMIP6 Models

Abstract The Atlantic meridional overturning circulation (AMOC) plays an important role in climate, transporting heat and salt to the subpolar North Atlantic. The AMOC’s variability is sensitive to atmospheric forcing, especially the North Atlantic Oscillation (NAO). Because AMOC observations are short, climate models are a valuable tool to study the AMOC’s variability. Yet, there are known issues with climate models, like uncertainties and systematic biases. To investigate this, preindustrial control experiments from models participating in the phase 6 of Coupled Model Intercomparison Project (CMIP6) are evaluated. There is a large, but correlated, spread in the models’ subpolar gyre mean surface temperature and salinity. By splitting models into groups of either a warm–salty or cold–fresh subpolar gyre, it is shown that warm–salty models have a lower sea ice cover in the Labrador Sea and, hence, enable a larger heat loss during a positive NAO. Stratification in the Labrador Sea is also weaker in warm–salty models, such that the larger NAO-related heat loss can also affect greater depths. As a result, subsurface density anomalies are much stronger in the warm–salty models than in those that tend to be cold and fresh. As these anomalies propagate southward along the western boundary, they establish a zonal density gradient anomaly that promotes a stronger delayed AMOC response to the NAO in the warm–salty models. These findings demonstrate how model mean state errors are linked across variables and affect variability, emphasizing the need for improvement of the subpolar North Atlantic mean states in models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

PMIP: Looking back to its first phase

The Paleoclimate Modelling Intercomparison Project celebrates its 30th anniversary in 2021. The first phase initiated systematic model-model and model-data comparisons for the Last Glacial Maximum and Mid-Holocene. Here, we describe the historical context of PMIP, the experiment design, and the project’s early impacts.

58 GEOSCIENCES↗
DOE OSTI2022

Interpreting machine learning prediction of fire emissions and comparison with FireMIP process-based models

Annual burned areas in the United States have increased 2-fold during the past decades. With more large fires resulting in more emissions of fine particulate matter, an accurate prediction of fire emissions is critical for quantifying the impacts of fires on air quality, human health, and climate. This study aims to construct a machine learning (ML) model with game-theory interpretation to predict monthly fire emissions over the contiguous US (CONUS) and to understand the controlling factors of fire emissions. The optimized ML model is used to diagnose the process-based models in the Fire Modeling Intercomparison Project (FireMIP) to inform future development. Results show promising performance for the ML model, Community Land Model (CLM), and Joint UK Land Environment Simulator-Interactive Fire And Emission Algorithm For Natural Environments (JULES-INFERNO) in reproducing the spatial distributions, seasonality, and interannual variability of fire emissions over the CONUS. Regional analysis shows that only the ML model and CLM simulate the realistic interannual variability of fire emissions for most of the subregions (r >0.95 for ML and r =0.14~0.70 for CLM), except for Mediterranean California, where all the models perform poorly (r =0.74 for ML and r <0.30 for the FireMIP models). Regarding seasonality, most models capture the peak emission in July over the western US. However, all models except for the ML model fail to reproduce the bimodal peaks in July and October over Mediterranean California, which may be explained by the smaller wind speeds of the atmospheric forcing data during Santa Ana wind events and limitations in model parameterizations for capturing the effects of Santa Ana winds on fire activity. Furthermore, most models struggle to capture the spring peak in emissions in the southeastern US, probably due to underrepresentation of human effects and the influences of winter dryness on fires in the models. As for extreme events, both the ML model and CLM successfully reproduce the frequency map of extreme emission occurrence but overestimate the number of months with extremely large fire emissions. Comparing the fire PM 2.5 emissions from the ML model with process-based fire models highlights their strengths and uncertainties for regional analysis and prediction and provides useful insights into future directions for model improvements.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Development of inter-grid-cell lateral unsaturated and saturated flow model in the E3SM Land Model (v2.0)

Abstract. The lateral transport of water in the subsurface is important in modulating terrestrial water energy distribution. Although a few land surface models have recently included lateral saturated flow within and across grid cells, it is not a default configuration in the Climate Model Intercomparison Project version 6 experiments. In this work, we developed the lateral subsurface flow model within both unsaturated and saturated zones in the Energy Exascale Earth System Model (E3SM) Land Model version 2 (ELMv2.0). The new model, called ELMlat, was benchmarked against PFLOTRAN, a 3D subsurface flow and transport model, for three idealized hillslopes that included a convergent hillslope, divergent hillslope, and tilted V-shaped hillslope with variably saturated initial conditions. ELMlat showed comparable performance against PFLOTRAN in terms of capturing the dynamics of soil moisture and groundwater table for the three benchmark hillslope problems. Specifically, the mean absolute errors (MAEs) of the soil moisture in the top 10 layers between ELMlat and PFLOTRAN were within 1 %±3 %, and the MAEs of water table depth were within ±0.2 m. Next, ELMlat was applied to the Little Washita experimental watershed to assess its prediction of groundwater table, soil moisture, and soil temperature. The spatial pattern of simulated groundwater table depth agreed well with the global groundwater table benchmark dataset generated from a global model calibrated with long-term observations. The effects of lateral groundwater flow on the energy flux partitioning were more prominent in lowland areas with shallower groundwater tables, where the difference in simulated annual surface soil temperature could reach 0.3–0.4 ∘C between ELMv2.0 and ELMlat. Incorporating lateral subsurface flow in ELM improves the representation of the subsurface hydrology, which will provide a good basis for future large-scale applications.

58 GEOSCIENCES↗
DOE OSTI2024

Hector V3.2.0: functionality and performance of a reduced-complexity climate model

Abstract. Hector is an open-source reduced-complexity climate–carbon cycle model that models critical Earth system processes on a global and annual basis. Here, we present an updated version of the model, Hector V3.2.0 (hereafter Hector V3), and document its new features, implementation of new science, and performance. Significant new features include permafrost thaw, a reworked energy balance submodel, and updated parameterizations throughout. Hector V3 results are in good general agreement with historical observations of atmospheric CO2 concentrations and global mean surface temperature, and the future temperature projections from Hector V3 are consistent with more complex Earth system model output data from the sixth phase of the Coupled Model Intercomparison Project. We show that Hector V3 is a flexible, performant, robust, and fully open-source simulator of global climate changes. We also note its limitations and discuss future areas for improvement and research with respect to the model's scientific, stakeholder, and educational priorities.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2015

Inconsistent Strategies to Spin up Models in CMIP5: Implications for Ocean Biogeochemical Model Performance Assessment

During the fifth phase of the Coupled Model Intercomparison Project (CMIP5) substantial efforts were made to systematically assess the skill of Earth system models. One goal was to check how realistically representative marine biogeochemical tracer distributions could be reproduced by models. In routine assessments model historical hindcasts were compared with available modern biogeochemical observations. However, these assessments considered neither how close modeled biogeochemical reservoirs were to equilibrium nor the sensitivity of model performance to initial conditions or to the spin-up protocols. Here, we explore how the large diversity in spin-up protocols used for marine biogeochemistry in CMIP5 Earth system models (ESMs) contributes to model-to-model differences in the simulated fields. We take advantage of a 500-year spin-up simulation of IPSL-CM5A-LR to quantify the influence of the spin-up protocol on model ability to reproduce relevant data fields. Amplification of biases in selected biogeochemical fields (O2, NO3, Alk-DIC) is assessed as a function of spin-up duration. We demonstrate that a relationship between spin-up duration and assessment metrics emerges from our model results and holds when confronted with a larger ensemble of CMIP5 models. This shows that drift has implications for performance assessment in addition to possibly aliasing estimates of climate change impact. Our study suggests that differences in spin-up protocols could explain a substantial part of model disparities, constituting a source of model-to- model uncertainty. This requires more attention in future model intercomparison exercises in order to provide quantitatively more correct ESM results on marine biogeochemistry and carbon cycle feedbacks.

histories↗
DOE OSTI2024

On the predictability of turbulent fluxes from land: PLUMBER2 MIP experimental description and preliminary results

Accurate representation of the turbulent exchange of carbon, water, and heat between the land surface and the atmosphere is critical for modelling global energy, water, and carbon cycles in both future climate projections and weather forecasts. Evaluation of models' ability to do this is performed in a wide range of simulation environments, often without explicit consideration of the degree of observational constraint or uncertainty and typically without quantification of benchmark performance expectations. We describe a Model Intercomparison Project (MIP) that attempts to resolve these shortcomings, comparing the surface turbulent heat flux predictions of around 20 different land models provided with in situ meteorological forcing evaluated with measured surface fluxes using quality-controlled data from 170 eddy-covariance-based flux tower sites. Predictions from seven out-of-sample empirical models are used to quantify the information available to land models in their forcing data and so the potential for land model performance improvement. Sites with unusual behaviour, complicated processes, poor data quality, or uncommon flux magnitude are more difficult to predict for both mechanistic and empirical models, providing a means of fairer assessment of land model performance. When examining observational uncertainty, model performance does not appear to improve in low-turbulence periods or with energy-balance-corrected flux tower data, and indeed some results raise questions about whether the energy balance correction process itself is appropriate. In all cases the results are broadly consistent, with simple out-of-sample empirical models, including linear regression, comfortably outperforming mechanistic land models. In all but two cases, latent heat flux and net ecosystem exchange of CO 2 are better predicted by land models than sensible heat flux, despite it seeming to have fewer physical controlling processes. Land models that are implemented in Earth system models also appear to perform notably better than stand-alone ecosystem (including demographic) models, at least in terms of the fluxes examined here. The approach we outline enables isolation of the locations and conditions under which model developers can know that a land model can improve, allowing information pathways and discrete parameterisations in models to be identified and targeted for future model development.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Prioritizing the selection of CMIP6 model ensemble members for downscaling projections of CONUS temperature and precipitation

Given the mismatch between the large volume of data archived for the sixth phase of the Coupled Model Intercomparison Project (CMIP6) and limited personnel and computational resources for downscaling, only a small fraction of the CMIP6 archive can be downscaled. In this work, we develop an approach to robustly sample projected hydroclimate states in CMIP6 for downscaling to test whether the selection of a single initial condition (IC) ensemble member from each CMIP6 model is sufficient to span the range of modeled hydroclimate over the conterminous United States (CONUS) and CONUS sub-regions. We calculate the pattern-centered root mean square difference of IC ensemble member anomalies relative to each model’s historical climatology for shared socioeconomic pathway (SSP) projections over 30-year time periods and compare the ratio of inter-model to intra-model variability for this metric. Regardless of SSP, inter-model variability is generally much greater than intra-model variability at the scales of the CONUS as a whole, as well as for most CONUS sub-regions. However, for some variables and scenarios, inter- and intra-model variability are similar at sub-CONUS scales, indicating that selecting a single IC ensemble member per model may be sufficient to sample the range of projected hydroclimate states in the 21st Century across CONUS, but for specific regions and variables, more careful selection of ensemble members may be necessary. Regionally resolved Taylor diagrams identify where more IC ensemble member downscaling efforts should be focused if resources are available to do so. Our results suggest that, with parsimonious sampling, the requisite computational expense of downscaling temperature and precipitation fields over the CONUS for subsequent CMIP activities may increase only marginally despite the great increase in data volumes with each successive CMIP phase.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2013

Simulations of the Mid-Pliocene Warm Period Using Two Versions of the NASA-GISS ModelE2-R Coupled Model

The mid-Pliocene Warm Period (mPWP) bears many similarities to aspects of future global warming as projected by the Intergovernmental Panel on Climate Change (IPCC, 2007). Both marine and terrestrial data point to high-latitude temperature amplification, including large decreases in sea ice and land ice, as well as expansion of warmer climate biomes into higher latitudes. Here we present our most recent simulations of the mid-Pliocene climate using the CMIP5 version of the NASAGISS Earth System Model (ModelE2-R). We describe the substantial impact associated with a recent correction made in the implementation of the Gent-McWilliams ocean mixing scheme (GM), which has a large effect on the simulation of ocean surface temperatures, particularly in the North Atlantic Ocean. The effect of this correction on the Pliocene climate results would not have been easily determined from examining its impact on the preindustrial runs alone, a useful demonstration of how the consequences of code improvements as seen in modern climate control runs do not necessarily portend the impacts in extreme climates.Both the GM-corrected and GM-uncorrected simulations were contributed to the Pliocene Model Intercomparison Project (PlioMIP) Experiment 2. Many findings presented here corroborate results from other PlioMIP multi-model ensemble papers, but we also emphasize features in the ModelE2-R simulations that are unlike the ensemble means. The corrected version yields results that more closely resemble the ocean core data as well as the PRISM3D reconstructions of the mid-Pliocene, especially the dramatic warming in the North Atlantic and Greenland-Iceland-Norwegian Sea, which in the new simulation appears to be far more realistic than previously found with older versions of the GISS model. Our belief is that continued development of key physical routines in the atmospheric model, along with higher resolution and recent corrections to mixing parameterisations in the ocean model, have led to an Earth System Model that will produce more accurate projections of future climate.

climate change↗
DOE OSTI2025

PCMDI Metrics Package

The Program for Climate Model Diagnosis & Intercomparison (PCMDI) Metrics Package (PMP) is used to provide "quick-look" objective comparisons of Earth System Models (ESMs) with one another and available observations. The PMP provides a diverse suite of analysis utilities each of which produce summary statistics that gauge the consistency between climate model simulations and available observations. The primary application of the PMP is to evaluate simulations from the Coupled Model Intercomparison Project (CMIP). It can also be used to provide objective performance summaries during the model development process as well as selected research purposes.

Ullrich, PaulA [Lawrence Livermore National Labora↗