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DOE OSTI2021

Future projections in the climatology of global low-level jets from CORDEX-CORE simulations

The potential changes in the strength and location of five low-level jets (LLJs) located within four Coordinated Regional Climate Downscaling Experiment (CORDEX) domains are examined for present and future climate conditions using an ensemble of simulations conducted with the RegCM4 regional model at a 25 km horizontal grid spacing. Lateral and lower boundary forcing fields are from three General Circulation Models (GCMs), and we analyse a historical period (1995–2014) along with two future periods (2041–2060 and 2080–2099) under the Representative Concentration Pathways 2.6 and 8.5. The RegCM4, as driven by the GCMs, is capable of capturing most of the observed climatological features of the LLJs, both in terms of spatial location and seasonal evolution. Analysis of the influence of global warming on the LLJs shows a consistent strengthening of the jets and a shift in their location under both warming scenarios. The Monsoon and West African westerly LLJs exhibit a northward shift, while the Caribbean and South American LLJs present a westward expansion. The use of an ensemble of high-resolution simulations is found to provide a key element for a robust assessment of changes in LLJs associated with future global warming scenarios.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Robust late twenty-first century shift in the regional monsoons in RegCM-CORDEX simulations

Abstract We use an unprecedented ensemble of regional climate model (RCM) projections over seven regional CORDEX domains to provide, for the first time, an RCM-based global view of monsoon changes at various levels of increased greenhouse gas (GHG) forcing. All regional simulations are conducted using RegCM4 at a 25 km horizontal grid spacing using lateral and lower boundary forcing from three General Circulation Models (GCMs), which are part of the fifth phase of the Coupled Model Inter-comparison Project (CMIP5). Each simulation covers the period from 1970 through 2100 under two Representative Concentration Pathways (RCP2.6 and RCP8.5). Regional climate simulations exhibit high fidelity in capturing key characteristics of precipitation and atmospheric dynamics across monsoon regions in the historical period. In the future period, regional monsoons exhibit a spatially robust delay in the monsoon onset, an increase in seasonality, and a reduction in the rainy season length at higher levels of radiative forcing. All regions with substantial delays in the monsoon onset exhibit a decrease in pre-monsoon precipitation, indicating a strong connection between pre-monsoon drying and a shift in the monsoon onset. The weakening of latent heat driven atmospheric warming during the pre-monsoon period delays the overturning of atmospheric subsidence in the monsoon regions, which defers their transitioning into deep convective states. Monsoon changes under the RCP2.6 scenario are mostly within the baseline variability.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Xanthos Output Dataset Under ISIMIP3b Selected CMIP6 Scenarios: 1850 - 2100 (Basin and Regional Scale)

Xanthos is an open-source hydrologic model, written in Python, designed to quantify and analyze global water availability. Xanthos simulates historical and future global water availability on a monthly time step at a spatial resolution of 0.5 geographic degrees. Xanthos was designed to be extensible and used by scientists that study global water supply and work with the Global Change Analysis Model (GCAM). Xanthos Output Dataset This dataset includes Xanthos outputs forced by the ISIMIP3b bias-adjusted CMIP6 climate forcing (1850 - 2100). There are 30 scenarios consists of 10 CMIP6 GCMs and 3 SSP-RCP scenarios. (1) 10 CMIP6 GCMs: GFDL-ESM4, IPSL-CM6A-LR, MPI-ESM1-2-HR, MRI-ESM2-0, UKESM1-0-LL, CanESM5, CNRM-CM6-1, CNRM-ESM2-1, EC-Earth3, and MIROC6 (2) 3 SSP-RCP Scenarios: SSP1-2.6, SSP3-7.0, and SSP5-8.5 The output variables in this dataset are aggregated to basin and regional scales. (1) Monthly runoff at basin scale (2) Annual accessible renewable water at basin scale (2) Annual actual hydropower production, hydropower potential, and exploitable hydropower potential at regional scale

Climate Change↗
DOE OSTI2023

Climate Impacts of Convective Cloud Microphysics in NCAR CAM5

Here we improved the treatments of convective cloud microphysics in the NCAR Community Atmosphere Model version 5.3 (CAM5.3) by 1) implementing new terminal velocity parameterizations for convective ice and snow particles, 2) adding graupel microphysics, 3) considering convective snow detrainment, and 4) enhancing rain initiation and generation rate in warm clouds. Furthermore, we evaluated the impacts of improved microphysics on simulated global climate, focusing on simulated cloud radiative forcing, graupel microphysics, convective cloud ice amount, and tropical precipitation. Compared to CAM5.3 with the default convective microphysics, the too-strong cloud shortwave radiative forcing due primarily to excessive convective cloud liquid is largely alleviated over the tropics and midlatitudes after rain initiation and generation rate is enhanced, in better agreement with the CERES-EBAF estimates. Geographic distributions of graupel occurrence are reasonably simulated over continents; whereas the graupel occurrence remains highly uncertain over the oceanic storm-track regions. When evaluated against the CloudSat–CALIPSO estimates, the overestimation of convective ice mass is alleviated with the improved convective ice microphysics, among which adding graupel microphysics and the accompanying increase in hydrometeor fall speed play the most important role. The probability distribution function (PDF) of rainfall intensity is sensitive to warm rain processes in convective clouds, and enhancement in warm rain production shifts the PDF toward heavier precipitation, which agrees better with the TRMM observations. Common biases of overestimating the light rain frequency and underestimating the heavy rain frequency in GCMs are mitigated.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Floods of Egypt’s Nile in the 21st century

Extreme precipitation and flooding events are rising globally, necessitating a thorough understanding and sustainable management of water resources. One such setting is the Nile River’s source areas, where high precipitation has led to the filling of Lake Nasser (LN) twice (1998–2003; 2019–2022) in the last two decades and the diversion of overflow to depressions west of the Nile, where it is lost mainly to evaporation. Using temporal satellite-based data, climate models, and continuous rainfall-runoff models, we identified the primary contributor to increased runoff that reached LN in the past two decades and assessed the impact of climate change on the LN’s runoff throughout the twenty-first century. Findings include: (1) the Blue Nile subbasin (BNS) is the primary contributor to increased downstream runoff, (2) the BNS runoff was simulated in the twenty-first century using a calibrated (1965–1992) rainfall-runoff model with global circulation models (GCMs), CCSM4, HadGEM3, and GFDL-CM4.0, projections as model inputs, (3) the extreme value analysis for projected runoff driven by GCMs’ output indicates extreme floods are more severe in the twenty-first century, (4) one adaptation for the projected twenty-first century increase in precipitation (25–39%) and flood (2%-20%) extremes is to recharge Egypt’s fossil aquifers during high flood years.

Climate change↗
DOE OSTI2020

GCAM v4.3 SSP-RCP-GCM Output Products

Supplemental dataset to: Graham N.T., M.I. Hejazi, M. Chen, E. Davies, J.A. Edmonds, S.H. Kim, and S. Turner, et al. 2020. "Humans drive future water scarcity changes across all Shared Socioeconomic Pathways." Environmental Research Letters 15, no. 1:Article No. 014007. PNNL-SA-151297. doi:10.1088/1748-9326/ab639b This analysis makes deterministic classifications of how, when water resources are limited and constrained, the human and climate systems interact. The relative effects of both systems on water scarcity are quantified at global and basin scales across 15 global futures that include five different socioeconomic conditions (the Shared Socioeconomic Pathways, SSPs) and four different climatic conditions (the Representative Concentration Pathways, RCPs). These 15 scenarios are used to first analyze a 'Human Alone' component by isolating the human impact on future scarcity. This is accomplished by holding all climate variables to their 2005 levels while altering socioeconomic growth and technological change. General circulation model (GCM) derived climate impacts for five models are applied to establish 75 'Human and Climate' scenarios which allows for the quantification of climate impacts. Climatic impacts to water supply, agricultural productivity and change, hydropower availability, and building energy demands are applied from 5 different bias-corrected GCMs to make a suite of 75 climate runs. By subtracting the human derived impacts from the 'Human Alone' scenarios, from the 'Human and Climate' scenarios the climate impact is isolated. First, the drivers of future water scarcity are evaluated by isolating the impacts that both humans and climate have while accounting for feedbacks between humans, energy, and land. Secondly, the simultaneous impacts that human and climate systems have on water scarcity are analyzed by determining whether each system is increasing or decreasing scarcity in all global water basins. Below we describe the GCAM model, the scenario components, climate derived impacts, and the calculations of scarcity changes and attribution. This study uses the global change assessment model (GCAM), with inclusions of water constraints to both renewable and nonrenewable sources of water, to investigate the relative contributions of climate and human systems on water scarcity regionally and globally under a wide range of scenarios. GCAM links socioeconomics, the energy system, land-use change, climate, and the water sector. GCAM is a market-equilibrium model that allows for prices to be adjusted within each time step to ensure that the supply and demand of goods and services remains equilibrated at each time step allowing for simultaneous market clearing across sectors. This study accounts for a limited supply of water by employing cost resource curves across all 235 basins that follow a logit formulation to determine the share of each water source (renewable surface water, nonrenewable groundwater, and desalinated water) needed to meet the water demands within all basins. As depletion of various water sources increases the extraction price increases, which leads to compounding price increases on the goods and services that require higher-priced water sources.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2020

Evaluating and Improving Convective Parameterization for GCMs Using ARM Observations. Final report

This technical report summarizes the achievements during the project period funded by the Atmospheric System Research (ASR) program. The general goal of the project is to improve the representation of atmospheric deep convection in global climate models. By using DOE Atmospheric Radiation Measurement (ARM) program observations and a hierarchy of models from cloud-resolving to global climate models, we evaluated and improved many aspects of convection parameterization schemes, such as the trigger function for convection and closure condition to determine the amount of convection. We also incorporated the stochasticity of convection into a convection scheme. These improvements, when implemented into the National Center for Atmospheric Research (NCAR) Community Earth System Model (CESM) and the DOE Exascale Energy Earth System Model (E3SM), led to improved simulation of precipitation characteristics, including precipitation intensity, Intertropical Convergence Zone, and Madden-Julian oscillation. The project also improved the representation of microphysical processes in convective clouds. It enables the interaction of cloud microphysics with aerosols in weather and climate models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Collaborative Research: Improved Efficiency and Coupling of the Radiation Code in the ACME Earth System Model (Final Report)

The complexity of radiative transfer, its importance to the exchange of energy in the climate system, and its high computational cost establishes importance of an accurate and efficient radiative transfer parameterization for climate simulation. Previous work by the proposing team at AER led to the development of the radiation code, RRTMG, which has been widely accepted by the global modeling community as a fast and accurate advancement over the previous generation of radiation codes. It has been in use in the NCAR CESM for many years, and it has been implemented in the initial version the DOE E3SM model. However, its computational cost remains high relative to other components in part due to its complexity and to its inefficient use of modern optimization strategies, and this project helped address this limitation for the code’s application in E3SM. Under other funding, the Investigators of this project led an effort to develop a high-performance broadband radiation code, called RTE+RRTMGP, which is a completely restructured code that will take advantage of modern computational capabilities to enhance its performance while retaining the strengths and accuracy of the original code. Designed to perform over a range of computer architectures, RTE+RRTMGP makes extensive use of Fortran 2003 features to improve both its efficiency and its use of memory. RTE+RRTMGP is expected to be adopted in the next generation of RTE+RRTMGP. This project allowed for advancements to the code’s computational capabilities and adding new and enhanced features. This included revising RTE+RRTMGP to run on GPU processors, analysis of and improvements to the code’s timing, modifying the gas optics in RRTMGP to increase the code’s accuracy, extensive validation of computed fluxes, heating rates and forcings, generating a cloud optical property and vertical sampling capabilities for RTE+RRTMGP, implementing a fast and accurate longwave scattering capability, and groundwork for the inclusion of a capability to specify solar variability. Many of the accomplishment in this project necessitated significant collaboration with the E3SM development team. The result of this project was optimization of a key physical component (radiative transfer calculations) of E3SM, directly supporting E3SM’s overarching global modeling objectives. More broadly, this project provided overall advancements in the use of radiative transfer calculations in atmospheric modeling and simulation, particularly for climate.

58 GEOSCIENCES↗
DOE OSTI2020

Differential credibility assessment for statistical downscaling

Climate science is increasingly using (i) ensembles of climate projections from multiple models derived using different assumptions and/or scenarios and (ii) process-oriented diagnostics of model fidelity. Efforts to assign differential credibility to projections and/or models are also rapidly advancing. A framework to quantify and depict the credibility of statistically downscaled model output is presented and demonstrated. Here, the approach employs transfer functions in the form of robust and resilient generalized linear models applied to downscale daily minimum and maximum temperature anomalies at 10 locations using predictors drawn from ERA-Interim reanalysis and two global climate models (GCM; GFDL-ESM2M and MPI-ESM-LR). The downscaled time series are used to derive several impact relevant CLIMDEX temperature indices that are assigned credibility based on (1) the reproduction of relevant large-scale predictors by the GCMs (i.e. fraction of regression beta-weights derived from predictors that are well-reproduced) and (2) the degree of variance in the observations reproduced in the downscaled series following application of a new variance inflation technique. Credibility of the downscaled predictands varies across locations, between the two GCM and is generally higher for minimum temperature than maximum temperature. The differential credibility assessment framework demonstrated here is easy to use and flexible. It can be applied as is to inform decision makers regarding projection confidence, and/or extended to include other components of the transfer functions, and/or used to weight members of a statistically downscaled ensemble.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Improving Seasonal Forecast Using Probabilistic Deep Learning

The path toward realizing the potential of seasonal forecasting and its socioeconomic benefits relies on improving general circulation model (GCM) based dynamical forecast systems. To improve dynamical seasonal forecasts, it is crucial to set up forecast benchmarks, and clarify forecast limitations posed by model initialization errors, formulation deficiencies, and internal climate variability. With huge costs in generating large forecast ensembles, and limited observations for forecast verification, the seasonal forecast benchmarking and diagnosing task proves challenging. Here, we develop a probabilistic deep learning-based statistical forecast methodology, drawing on a wealth of climate simulations to enhance seasonal forecast capability and forecast diagnosis. By explicitly modeling the internal climate variability and GCM formulation differences, the proposed Conditional Generative Forecasting (CGF) methodology enables bypassing crucial barriers in dynamical forecast, and offers a top-down viewpoint to examine how complicated GCMs encode the seasonal predictability information. We apply the CGF methodology for global seasonal forecast of precipitation and 2 m air temperature, based on a unique data set consisting 52,201 years of climate simulation. Results show that the CGF methodology can faithfully represent the seasonal predictability information encoded in GCMs. We successfully apply this learned relationship in real-world seasonal forecast, achieving competitive performance compared to dynamical forecasts. Using this CGF as benchmark, we reveal the impact of insufficient forecast spread sampling that limits the skill of the considered dynamical forecast system. Finally, we introduce different strategies for composing ensembles using the CGF methodology, highlighting the potential for leveraging the strengths of multiple GCMs to achieve advantgeous seasonal forecast.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Interpreting and Stabilizing Machine-Learning Parametrizations of Convection

Neural networks are a promising technique for parameterizing subgrid-scale physics (e.g., moist atmospheric convection) in coarse-resolution climate models, but their lack of interpretability and reliability prevents widespread adoption. For instance, it is not fully understood why neural network parameterizations often cause dramatic instability when coupled to atmospheric fluid dynamics. This paper introduces tools for interpreting their behavior that are customized to the parameterization task. First, we assess the nonlinear sensitivity of a neural network to lower-tropospheric stability and the midtropospheric moisture, two widely studied controls of moist convection. Second, we couple the linearized response functions of these neural networks to simplified gravity wave dynamics, and analytically diagnose the corresponding phase speeds, growth rates, wavelengths, and spatial structures. To demonstrate their versatility, these techniques are tested on two sets of neural networks, one trained with a super-parameterized version of the Community Atmosphere Model (SPCAM) and the second with a near-global cloud-resolving model (GCRM). Additionally, even though the SPCAM simulation has a warmer climate than the cloud-resolving model, both neural networks predict stronger heating/drying in moist and unstable environments, which is consistent with observations. Moreover, the spectral analysis can predict that instability occurs when GCMs are coupled to networks that support gravity waves that are unstable and have phase speeds larger than 5 m s -1 . In contrast, standing unstable modes do not cause catastrophic instability. Using these tools, differences between the SPCAM-trained versus GCRM-trained neural networks are analyzed, and strategies to incrementally improve both of their coupled online performance unveiled.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

RCEMIP-II: mock-Walker simulations as phase II of the radiative–convective equilibrium model intercomparison project

Abstract. The radiative–convective equilibrium (RCE) model intercomparison project (RCEMIP) leveraged the simplicity of RCE to focus attention on moist convective processes and their interactions with radiation and circulation across a wide range of model types including cloud-resolving models (CRMs), general circulation models (GCMs), single-column models, global cloud-resolving models, and large-eddy simulations. While several robust results emerged across the spectrum of models that participated in the first phase of RCEMIP (RCEMIP-I), two points that stand out are (1) the strikingly large diversity in simulated climate states and (2) the strong imprint of convective self-aggregation on the climate state. However, the lack of consensus in the structure of self-aggregation and its response to warming is a barrier to understanding. Gaining a deeper understanding of convective aggregation and tropical climate will require reducing the degrees of freedom with which convection can vary. Therefore, we propose phase II of RCEMIP (RCEMIP-II) that utilizes a prescribed sinusoidal sea surface temperature (SST) pattern to provide a constraint on the structure of convection and move one critical step up the model hierarchy. This so-called “mock-Walker” configuration generates features that resemble observed tropical circulations. The specification of the mock-Walker protocol for RCEMIP-II is described, along with example results from one CRM and one GCM. RCEMIP-II will consist of five required simulations: three simulations with the same three mean SSTs as in RCEMIP-I but with an SST gradient and two additional simulations at one of the mean SSTs with different values of the SST gradients. We also test the sensitivity to the imposed SST gradient and the domain size. Under weak SST gradients, unforced self-aggregation emerges across the entire domain, similar to what was found in RCEMIP. As the SST gradient increases, the convective region narrows and is more confined to the warmest SSTs. At warmer mean SSTs and stronger SST gradients, low-frequency variability in the convective aggregation emerges, suggesting that simulations of at least 200 d may be needed to achieve robust equilibrium statistics in this configuration. Simulations with different domain sizes generally have similar mean statistics and convective structures, depending on the value of the SST gradient. The prescribed SST boundary condition is the only difference in the set-up between RCEMIP-II and RCEMIP-I, which enables comparison between the two; however, we also welcome participation in RCEMIP-II from models that did not participate in RCEMIP-I.

Geology↗
DOE OSTI2024

Relationship Between Tropical Cloud Feedback and Climatological Bias in Clouds

Abstract Global climate model (GCM) projections of future climate are uncertain largely due to a persistent spread in cloud feedback. This is despite efforts to reduce this model uncertainty through a variety of emergent constraints (ECs); with several studies suggesting an important role for present‐day biases in clouds. Here, we use three generations of GCMs to assess the value of climatological cloud metrics for constraining uncertainty in cloud feedback. We find that shortwave cloud radiative properties across the Southern Hemisphere extratropics are most robustly correlated with tropical cloud feedback (TCF). Using this relationship in conjunction with observations, we produce an EC that yields a TCF value of 0.52 ± 0.34 W/m 2 /K, which equates to a 34% reduction in uncertainty. Thus, we show that climatological cloud properties can be used to reduce uncertainty in how clouds will respond to future warming.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Is Bias Correction in Dynamical Downscaling Defensible?

Localized projections of 21st‐century hydroclimate variables obtained from downscaling Global Climate Model (GCM) output are central to informing regional impact assessments and infrastructure planning. Regional GCM biases can be significant and, for dynamical downscaling, can be addressed either before (a priori) or after (a posteriori) downscaling. However, a priori bias correction (APBC) has generally unexplored effects on climate change signals. Here we analyze dynamically downscaled solutions of CMIP6 GCMs over the Western U.S., with and without APBC, and quantify APBC's impact on climate change signals relative to other irreducible uncertainty sources. For temperature and precipitation, the uncertainty introduced by APBC is negligible compared to that arising from GCM choice or internal variability. Furthermore, APBC greatly reduces regional models' unrealistically high snow‐water‐equivalent (SWE) biases that result directly from GCM errors. We leverage this finding to encourage the dynamical downscaling community to adopt APBC as a standard operating procedure.

Risser, Mark D.↗
DOE OSTI2023

Xanthos Output Dataset Under ISIMIP3b Selected CMIP6 Scenarios: 1850 - 2100

Overview Xanthos is an open-source hydrologic model, written in Python, designed to quantify and analyze global water availability. Xanthos simulates historical and future global water availability on a monthly time step at a spatial resolution of 0.5 geographic degrees. Xanthos was designed to be extensible and used by scientists that study global water supply and work with the Global Change Analysis Model (GCAM). Details This dataset includes Xanthos outputs forced by the ISIMIP3b bias-adjusted CMIP6 climate forcing (1850 - 2100). There are 30 scenarios consists of 10 Coupled Model Intercomparison Project phase 6 (CMIP6) General Circulation Models (GCM) and 3 Shared Socioeconomic Pathways (SSP). Each folder is named as cmip6_{gcm}_{ensemble-member}_{ssp}. 10 CMIP6 GCMs: GFDL-ESM4, IPSL-CM6A-LR, MPI-ESM1-2-HR, MRI-ESM2-0, UKESM1-0-LL, CanESM5, CNRM-CM6-1, CNRM-ESM2-1, EC-Earth3, and MIROC6 3 SSP-RCP Scenarios: SSP1-2.6, SSP3-7.0, and SSP5-8.5 The table below summarizes the details of output variables within each scenario folder. | Output Variable | File Short Name | Spatial Resolution | Unit | | ----------------------------------------------- | -------------------------------------- | -------------------------------- | --------------------- | | Runoff | q | 0.5x0.5 degree | km3/month | | Runoff | Basin_runoff | GCAM basin | km3/month | | Runoff | Country_runoff | Country | km3/month | | Runoff | GCAMRegion_runoff | GCAM region | km3/month | | Streamflow | avgchflow | 0.5x0.5 degree | m3/second | | Potential Evapotranspiration | pet | 0.5x0.5 degree | km3/month | | Actual Evapotranspiration | aet | 0.5x0.5 degree | km3/month | | Accessible Water | accessible_water | GCAM basin | km3/year | | Hydropower Generation | actual_hydro | GCAM region | EJ/year |

Climate Change↗
DOE OSTI2023

Xanthos Output Dataset Under ISIMIP3b Selected CMIP6 Scenarios: 1850 - 2100

Overview Xanthos is an open-source hydrologic model, written in Python, designed to quantify and analyze global water availability. Xanthos simulates historical and future global water availability on a monthly time step at a spatial resolution of 0.5 geographic degrees. Xanthos was designed to be extensible and used by scientists that study global water supply and work with the Global Change Analysis Model (GCAM). Details This dataset includes Xanthos outputs forced by the ISIMIP3b bias-adjusted CMIP6 climate forcing (1850 - 2100). There are 30 scenarios consists of 10 Coupled Model Intercomparison Project phase 6 (CMIP6) General Circulation Models (GCM) and 3 Shared Socioeconomic Pathways (SSP). Each folder is named as cmip6_{gcm}_{ensemble-member}_{ssp}. 10 CMIP6 GCMs: GFDL-ESM4, IPSL-CM6A-LR, MPI-ESM1-2-HR, MRI-ESM2-0, UKESM1-0-LL, CanESM5, CNRM-CM6-1, CNRM-ESM2-1, EC-Earth3, and MIROC6 3 SSP-RCP Scenarios: SSP1-2.6, SSP3-7.0, and SSP5-8.5 The table below summarizes the details of output variables within each scenario folder. | Output Variable | File Short Name | Spatial Resolution | Unit | | ----------------------------------------------- | -------------------------------------- | -------------------------------- | --------------------- | | Runoff | q | 0.5x0.5 degree | km3/month | | Runoff | Basin_runoff | GCAM basin | km3/month | | Runoff | Country_runoff | Country | km3/month | | Runoff | GCAMRegion_runoff | GCAM region | km3/month | | Streamflow | avgchflow | 0.5x0.5 degree | m3/second | | Potential Evapotranspiration | pet | 0.5x0.5 degree | km3/month | | Actual Evapotranspiration | aet | 0.5x0.5 degree | km3/month | | Accessible Water | accessible_water | GCAM basin | km3/year | | Hydropower Generation | actual_hydro | GCAM region | EJ/year |

Climate Change↗
DOE OSTI2025

Analyzing the Impact of Future Weather Data on Energy Consumption in Weatherization Assistant

This study supports the mission of the U.S. Department of Energy’s Weatherization Assistance Program (WAP), which aims to increase the energy efficiency of dwellings and reduce their total residential expenditures. Specifically, we examine how projected future climate conditions may affect residential building energy performance by integrating future weather data into the National Energy Audit Tool (NEAT). Since WAP evaluates the cost-effectiveness of retrofit measures over lifespans of up to 30 years, accounting for evolving climate conditions is increasingly important. To reflect future household energy demands, this study replaces historically based Typical Meteorological Year (TMY3) weather inputs with Future Typical Meteorological Year (fTMY) datasets derived from global climate model (GCM) projections. A simulation-based framework was established to enable NEAT analysis under future weather conditions. This workflow involves converting EPW-format weather files into JSON inputs compatible with NEAT and generating degree-hour metrics needed for load calculations. The fTMY dataset used in this study was developed by Oak Ridge National Laboratory through downscaling of six GCMs under different emission scenarios and covers the period from 2020 to 2100. In contrast, the TMY3 dataset is based on historical weather data from 1961 to 1990. Simulations were conducted for benchmark single-family prototype buildings across ASHRAE climate zones 1–7, which cover all regions of the U.S. except the subarctic Zone 8 in northern Alaska, evaluating both heating and cooling loads under TMY3 and fTMY conditions. Four foundation types were tested, while heating systems were standardized, as NEAT does not differentiate thermal energy load by HVAC system type in its load calculations. Results show that fTMY weather input consistently yield lower heating loads and higher cooling loads across most locations, aligning with expected climate warming trends. Notably, colder regions such as zones 6A, 6B, and 7 experience marked reductions in heating load, while warmer and transitional zones, such as 2A (Lufkin, TX) and 3C (San Francisco, CA), have substantial increases in cooling loads. Although this study does not directly assess the performance of retrofit measures under future climate conditions, it provides a critical foundation for doing so. By quantifying shifts in baseline (i.e., pre-retrofit case) energy loads between historical and future weather files, the study highlights the importance of integrating climate-responsive data into audit tools. These findings will inform future efforts to evaluate the long-term effectiveness and cost-effectiveness of weatherization measures under changing climate conditions.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗