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At least 307 records · Page 17
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DOE OSTI2018

On the Momentum Budget of the Quasi-Biennial Oscillation in the Whole Atmosphere Community Climate Model

Abstract This study documents the contribution of equatorial waves and mesoscale gravity waves to the momentum budget of the quasi-biennial oscillation (QBO) in a 110-level version of the Whole Atmosphere Community Climate Model. The model has high vertical resolution, 500 m, above the boundary layer and through the lower and middle stratosphere, decreasing gradually to about 1.5 km near the stratopause. Parameterized mesoscale gravity waves and resolved equatorial waves contribute comparable easterly and westerly accelerations near the equator. Westerly acceleration by resolved waves is due mainly to Kelvin waves of zonal wavenumber in the range k = 1–15 and is broadly distributed about the equator. Easterly acceleration near the equator is due mainly to Rossby–gravity (RG) waves with zonal wavenumbers in the range k = 4–12. These RG waves appear to be generated in situ during both the easterly and westerly phases of the QBO, wherever the meridional curvature of the equatorial westerly jet is large enough to produce reversals of the zonal-mean barotropic vorticity gradient, suggesting that they are excited by the instability of the jet. The RG waves produce a characteristic pattern of Eliassen–Palm flux divergence that includes strong easterly acceleration close to the equator and westerly acceleration farther from the equator, suggesting that the role of the RG waves is to redistribute zonal-mean vorticity such as to neutralize the instability of the westerly jet. Insofar as unstable RG waves might be present in the real atmosphere, mixing due to these waves could have important implications for transport in the tropical stratosphere.

Garcia, Rolando R.↗
DOE OSTI2021

A Hybrid Climate Modeling System Using AI-assisted Process Emulators

This white paper addresses Focus Area II. We advocate developing a hybrid modeling system to improve the understanding of decadal- and longer-scale predictability of high impact water cycle components. This hybrid model combines a partial differential equation (PDE)-based dynamic core with AI/ML based emulators to represent many of the computationally expensive processes in Earth’s climate models. The hybrid modeling system has the potential to exploit emerging graphics processing unit (GPU)-accelerated architectures and allows for the generation of large ensemble (~1000’s) simulations to better characterize the model uncertainty and understand predictability.

58 GEOSCIENCES↗
DOE OSTI2012

Changes in winter precipitation extremes for the western United States under a warmer climate as simulated by regional climate models

We find a consistent and statistically significant increase in the intensity of future extreme winter precipitation events over the western United States, as simulated by an ensemble of regional climatemodels (RCMs) driven by IPCC AR4 global climate models (GCMs). All eight simulations analyzed in this work consistently show an increase in the intensity of extreme winter precipitation with the multi-model mean projecting an area-averaged 12.6% increase in 20-year return period and 14.4% increase in 50-year return period daily precipitation. In contrast with extreme precipitation, the multi-model ensemble shows a decrease in mean winter precipitation of approximately 7.5% in the southwestern US, while the interior west shows less statistically robust increases.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Land–Atmosphere Coupling at the U.S. Southern Great Plains: A Comparison on Local Convective Regimes between ARM Observations, Reanalysis, and Climate Model Simulations

Using the 9-yr warm-season observations at the Atmospheric Radiation Measurement Southern Great Plains site, in this work we assess the land–atmosphere (LA) coupling in the North American Regional Reanalysis (NARR) and two climate models: hindcasts with the Community Atmosphere Model version 5.1 by Cloud-Associated Parameterizations Testbed (CAM5-CAPT) and nudged runs with the Energy Exascale Earth System Model Atmosphere Model version 1 Regionally Refined Model (EAMv1-RRM). We focus on three local convective regimes and diagnose model behaviors using the local coupling metrics. NARR agrees well with observations except a slightly warmer and drier surface with higher downwelling shortwave radiation and lower evaporative fraction. On clear-sky days, it shows warmer and drier early-morning conditions in both models with significant underestimates in surface evaporation by EAMv1-RRM. On the majority of the ARM-observed shallow cumulus days, there is no or little low-level clouds in either model. When captured in models, the simulated shallow cumulus shows much less cloud fraction and lower cloud bases than observed. On the days with late-afternoon deep convection, models tend to present a stable early-morning lower atmosphere more frequently than the observations, suggesting that the deep convection is triggered more often by elevated instabilities. Generally, CAM5-CAPT can reproduce the local LA coupling processes to some extent due to the constrained early-morning conditions and large-scale winds. EAMv1-RRM exhibits large precipitation deficits and warm and dry biases toward mid-to-late summers, which may be an amplification through a positive LA feedback among initial atmosphere and land states, convection triggering and large-scale circulations.

58 GEOSCIENCES↗
DOE OSTI2018

Assessment of the cloud liquid water from climate models and reanalysis using satellite observations

We perform here a model-observation comparison and report on the state-of-the-art cloud liquid water content (CLWC) and path (CLWP) outputs from the present-day global climate models (GCMs) simulations in CMIP3/CMIP5, two other GCMs (UCLA and GEOS5) and two reanalyses (ECMWF Interim and MERRA) in comparison with two satellites observational datasets (CloudSat and MODIS). We use two different liquid water observation products from CloudSat and MODIS, for CLWP and their combined product for LWC with a method to remove the contribution from precipitating and convective core hydrometeors so that more meaningful model-observation comparisons can be made. Considering the CloudSat’s limitations of CLWC retrievals due to contamination from the precipitation and from radar clutter near the surface, an estimate CLWC is synergistically constructed using MODIS CLWP and CloudSat CLWC. The model-observation comparison shows that most of the CMIP3/CMIP5 annual mean CLWP values are overestimated by factors of 2 - 10 compared to observations globally. There are a number of CMIP5 models, including CSIRO, MPI, and the UCLA GCM that perform well compared to the other models. For the vertical structure of CLWC, significant systematic biases are found with many models biased significantly high above the mid-troposphere. In the tropics, systematic high biases occur at all levels above 700 hPa. Based on the Taylor diagram, the ensemble performance of CMIP5 CLWP simulation shows little or no improvement relative to CMIP3.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Connecting Large‐Scale Meteorological Patterns to Extratropical Cyclones in CMIP6 Climate Models Using Self‐Organizing Maps

Abstract Extratropical cyclones (ETCs) are responsible for the majority of cool‐season extreme events in the northeastern United States (NEUS), often leading to high‐impact weather conditions that can have wide‐ranging socioeconomic impacts. Evaluating the ability of climate models to adequately simulate ETC dynamics is essential for improving model performance and increasing confidence in future projections used by stakeholders and policymakers. ETCs are traditionally studied using techniques such as case studies and synoptic typing, however, these approaches can be time‐consuming, require subjective analysis, and do not necessarily identify the coincident large‐scale meteorological patterns (LSMPs). Here, we apply self‐organizing maps (SOMs) as an automated machine‐learning approach to characterize the LSMPs and associated frequency and intensity of discrete ETC events over NEUS. The dominant patterns of geopotential height variability are identified through SOM analysis of five reanalysis products during the last four decades. ETC events are tracked using TempestExtremes and are integrated with SOMs to classify the accumulated cyclone activity (ACA) associated with each pattern. We then evaluate the skill of CMIP6 historical experiments in simulating the LSMPs and ETC events identified in the SOM. Our results identify a robust bias toward more zonal patterns, with models struggling to reproduce the more amplified patterns typically associated with the highest cyclone activity. While model resolution has some impact on simulation credibility, model configuration appears to be more important in LSMP representation. The vast majority of CMIP6 models produce too few ETCs, although model errors are distributed around historical reanalyses when ACA is normalized by storm frequency.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Soil Moisture Drought Monitoring and Forecasting Using Satellite and Climate Model Data over Southwestern China

In this paper, an experimental soil moisture drought monitoring and seasonal forecasting framework based on the Variable Infiltration Capacity model (VIC) over southwestern China (SW) is presented. Satellite precipitation data are used to force VIC for a near-real-time estimate of land surface hydrologic conditions. Initialized with satellite-aided monitoring (MONIT), the climate model (CFSv2)-based forecast (MONIT+CFSv2) and ensemble streamflow prediction (ESP)-based forecast (MONIT+ESP) are both performed. One dry season drought and one wet season drought are employed to test the ability of this framework in terms of real-time tracking and predicting the evolution of soil moisture (SM) drought, respectively. The results show that the skillful CFSv2 climate forecasts (CFs) are only found at the first month. The satellite-aided monitoring is able to provide a reasonable estimate of forecast initial conditions (ICs) in real-time mode. In the presented cases, MONIT+CFSv2 forecast exhibits comparable performance against the observation-based estimates for the first month, whereas the predictive skill largely drops beyond 1 month. Compared to MONIT+ESP, MONIT+CFSv2 ensembles give more skillful SM drought forecast during the dry season, as indicated by a smaller ensemble range, while the added value of MONIT+CFSv2 is marginal during the wet season. Furthermore, a quantitative attribution analysis of SM forecast uncertainty demonstrates that SM forecast skill is mostly controlled by ICs at the first month and that uncertainties in CFs have the largest contribution to SM forecast errors at longer lead times. This study highlights a value of this framework in generating near-real-time ICs and providing a reliable SM drought prediction with 1 month ahead, which may greatly benefit drought diagnosis, assessment, and early warning.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Future changes in regional precipitation simulated by a half-degree coupled climate model: Sensitivity to horizontal resolution

The global fully coupled half-degree Community Climate System Model Version 4 (CCSM4) was integrated for a suite of climate change ensemble simulations including five historical runs, five Representative Concentration Pathway 8.5 [RCP8.5) runs, and a long Pre-Industrial control run. This study focuses on precipitation at regional scales and its sensitivity to horizontal resolution. The half-degree historical CCSM4 simulations are compared to observations, where relevant, and to the standard 1° CCSM4. Both the halfdegree and 1° resolutions are coupled to a nominal 1° ocean. North American and South Asian/Indian monsoon regimes are highlighted because these regimes demonstrate improvements due to higher resolution, primarily because of better-resolved topography. Agriculturally sensitive areas are analyzed and include Southwest, Central, and Southeast U.S., Southern Europe, and Australia. Both mean and extreme precipitation is discussed for convective and large-scale precipitation processes. Convective precipitation tends to decrease with increasing resolution and large-scale precipitation tends to increase. Improvements for the half-degree agricultural regions can be found for mean and extreme precipitation in the Southeast U.S., Southern Europe, and Australian regions. Climate change responses differ between the model resolutions for the U.S. Southwest/Central regions and are seasonally dependent in the Southeast and Australian regions. Both resolutions project a clear drying signal across Southern Europe due to increased greenhouse warming. As a result, differences between resolutions tied to the representation of convective and large-scale precipitation play an important role in the character of the climate change and depend on regional influences.

58 GEOSCIENCES↗
DOE OSTI2021

Global Temperature Responses to Large Tropical Volcanic Eruptions in Paleo Data Assimilation Products and Climate Model Simulations Over the Last Millennium

Abstract Large volcanic eruptions are one of the dominant perturbations to global and regional atmospheric temperatures on timescales of years to decades. Discrepancies remain, however, in the estimated magnitude and persistence of the surface temperature cooling caused by volcanic eruptions, as characterized by paleoclimatic proxies and climate models. We investigate these discrepancies in the context of large tropical eruptions over the Last Millennium using two state‐of‐the‐art data assimilation products, the Paleo Hydrodynamics Data Assimilation product (PHYDA) and the Last Millennium Reanalysis (LMR), and simulations from the National Center for Atmospheric Research Community Earth System Model‐Last Millennium Ensemble (NCAR CESM‐LME). We find that PHYDA and LMR estimate mean global and hemispheric cooling that is similar in magnitude and persistence once effects from eruptions occurring in short succession are removed. The estimates also compare well to Northern‐Hemisphere reconstructions based solely or partially on tree‐ring density, which have been proposed as the most accurate proxy estimates of surface cooling due to volcanism. All proxy‐based estimates also agree well with the magnitude of the mean cooling simulated by the CESM‐LME. Differences remain, however, in the spatial patterns of the temperature responses in the PHYDA, LMR, and the CESM‐LME. The duration of cooling anomalies also persists for several years longer in the PHYDA and LMR relative to the CESM‐LME. Our results demonstrate progress in resolving discrepancies between proxy‐ and model‐based estimates of temperature responses to volcanism, but also indicate these estimates must be further reconciled to better characterize the risks of future volcanic eruptions.

Tejedor, E.↗
DOE OSTI2020

ARM Data-Oriented Metrics and Diagnostics Package for Climate Model Evaluation

A Python-based metrics and diagnostics package is currently being developed by the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) user facility Infrastructure Team at Lawrence Livermore National Laboratory to facilitate the use of long-term, high-frequency measurements from ARM in evaluating the regional climate simulation of clouds, radiation, and precipitation. This metrics and diagnostics package computes climatological means of targeted climate model simulation and generates tables and plots for comparing the model simulation with ARM observational data.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Assessment of Storm-Associated Precipitation and Its Extremes Using Observational Data Sets and Climate Model Short-Range Hindcasts

Heavy precipitation, often associated with weather phenomena such as tropical cyclones, extratropical cyclones (ETCs), atmospheric rivers (ARs), and mesoscale convective systems (MCSs), can cause significant socio-economic loss. Here, in this study, we apply atmospheric feature trackers to quantify the contributions of these storm types in observational data sets and climate model short-range hindcasts. We generate a global hourly storm data set at 0.25° spatial resolution covering 2006–2020, based on the tracking results from TempestExtremes and Python FLEXible object TRacKeR. Our analyses show that these four storm types account for 67% of global annual mean precipitation and 82% of top 1% precipitation extremes, with MCSs mainly over the tropics, and ARs and ETCs over the midlatitudes. The percentage of precipitation contributions from these storms also show strong seasonality over many geographical locations. We further apply the tracking results to the Energy Exascale Earth System Model (E3SM) short-range hindcasts and evaluate how well these storms are simulated. The evaluation show that E3SM, with ∼1° resolution, significantly underestimates storm-associated precipitation totals and extremes, especially for MCSs in the tropics. Our analysis also suggests that model fails to capture the correct mean diurnal phases and amplitude of MCS precipitation. This phenomenon-based approach provides a better understanding of precipitation characteristics and can lead to enhanced model evaluation by revealing underlying problems in model physics related to precipitation processes associated with the heavy-precipitating storms.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Effects of Urbanization in China on the East Asian Summer Monsoon as Revealed by Two Global Climate Models

Abstract The East Asian summer monsoon (EASM), vulnerable to anthropogenic activities, exerts profound impacts on precipitation in East Asia. In recent decades, China has been experiencing rapid urbanization. It has not yet reached a consensus on its influences on EASM. Here, we introduce satellite‐derived urban impervious areas of China during 1985–2017 into two global climate models with contrasting biases of climatological EASM simulation. Both models consistently show that urbanization weakens EASM by increasing surface friction drag. The increased surface roughness reduces climatological southerly winds in the concentrated urbanization area. As a result, moisture convergence occurs south of the concentrated urbanization area with ascending motion, while moisture divergence with descending motion appears to the north, resulting in the precipitation anomalies of “southern flood and northern drought.” The dominant role of urban dynamic forcing in regulating EASM is confirmed by reducing urban roughness to the level of croplands in sensitivity experiments. As urban dynamic forcing is largely mitigated, the urbanization impact mainly driven by urban thermal forcing conversely strengthens EASM due to enhanced land‐sea thermal contrast via urbanization‐induced warming. Spatial patterns and magnitudes of the urbanization‐induced EASM weakening vary between the two models due to disparities in their simulations of urban thermodynamic forcing and atmospheric adjustments. These results underscore the robust weakening effect of urbanization on EASM by urban friction drag and document uncertainties in the related spatial pattern and magnitude resulting from model discrepancies.

Meteorology & Atmospheric Sciences↗
DOE OSTI2021

Developing and Testing a Novel Stochastic Ice Microphysics Parameterization for Cloud and Climate Models Using ARM Field Campaign Data (Final Progress Report)

The major goals of this project were: 1) to use field campaign measurements from DOE’s Atmospheric Radiation Measurement (ARM) program to characterize variability of important parameters describing properties of ice particles in the atmosphere; 2) based on this observational analysis, to develop a parameterization scheme for weather and climate models that stochastically varies these parameters, and implement the new scheme into a weather model called the Weather Research and Forecasting model (WRF); 3) to use WRF coupled with the new stochastic scheme to simulate ARM field campaign thunderstorm cases and analyze how accounting for this parameter variability affects the model simulations. This work was performed jointly between the National Center for Atmospheric Research, University of Oklahoma, and University of Utah. To accomplish these goals, we extended an approach previously developed to characterize the variability in the size distribution of ice particles to parameters that are explicitly represented in models (i.e., relationships between ice particle mass and size, and between particle fall velocity and size). Our project was, to our knowledge, the first to apply observationally-constrained estimates of this parameter variability describing mass-size and fall velocity-size in a modeling framework. Our results showed efficacy of the approach, evaluated using ARM observations. Similarly, to our knowledge, work in this project was the first to propose and evaluate in detail a stochastic approach for unresolved turbulent mixing in high-resolution model simulations against detailed, benchmark large eddy simulations and ARM observations. Results showed some promising behavior, particularly with increased mixing and dilution of air in thunderstorm cores with surrounding environmental air, bringing the stochastic simulations closer to the benchmark large eddy simulations; however, results were somewhat degraded using stochastic mixing compared to observations from the AMIE/DYNAMO field campaign. This project also further refined and applied a modeling methodology called “piggybacking” that can robustly separate dynamical and thermodynamic impacts of model changes, and comparison studies of different models based on cases developed from ARM observations. Finally, this project directly supported three graduate students who completed their PhDs as well as a postdoctoral research fellow.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Toward a new parameterization of hydraulic conductivity in climate models: Simulation of rapid groundwater fluctuations in N orthern C alifornia

Abstract Preferential flow through weathered bedrock leads to rapid rise of the water table after the first rainstorms and significant water storage (also known as “rock moisture”) in the fractures. We present a new parameterization of hydraulic conductivity that captures the preferential flow and is easy to implement in global climate models. To mimic the naturally varying heterogeneity with depth in the subsurface, the model represents the hydraulic conductivity as a product of the effective saturation and a background hydraulic conductivity K bkg , drawn from a lognormal distribution. The mean of the background K bkg decreases monotonically with depth, while its variance reduces with the effective saturation. Model parameters are derived by assimilating into Richards’ equation 6 years of 30 min observations of precipitation (mm) and water table depths (m), from seven wells along a steep hillslope in the Eel River watershed in Northern California. The results show that the observed rapid penetration of precipitation and the fast rise of the water table from the well locations, after the first winter rains, are well captured with the new stochastic approach in contrast to the standard van Genuchten model of hydraulic conductivity, which requires significantly higher levels of saturated soils to produce the same results. “Rock moisture,” the moisture between the soil mantle and the water table, comprises 30% of the moisture because of the great depth of the weathered bedrock layer and could be a potential source of moisture to sustain trees through extended dry periods. Furthermore, storage of moisture in the soil mantle is smaller, implying less surface runoff and less evaporation, with the proposed new model.

58 GEOSCIENCES↗
DOE OSTI2020

Clouds and Radiation Processes in Regional Climate Models Evaluated Using Observations Over the Ice‐free Arctic Ocean

Abstract The presence of clouds in the Arctic regulates the surface energy budget (SEB) over the sea‐ice surface and the ice‐free ocean. Following several previous field campaigns, the cloud‐radiation relationship, including cloud vertical structure and phase, has been elucidated; however, modeling of this relationship has matured slowly. In recognition of the recent decline in the Arctic sea‐ice extent, representation of the cloud system in numerical models should consider the effects of areas covered by sea ice and ice‐free areas. Using an in situ stationary meteorological observation data set obtained over the ice‐free Arctic Ocean by the Japanese Research Vessel Mirai (September 2014), coordinated evaluation of six regional climate models (RCMs) with nine model runs was performed by focusing on clouds and the SEB. The most remarkable findings were as follows: (1) reduced occurrence of unstable stratification with low‐level cloud water in all models in comparison to the observations, (2) significant differences in cloud water representations between single‐ and double‐moment cloud schemes, (3) extensive differences in partitioning of hydrometeors including solid/liquid precipitation, and (4) pronounced lower‐tropospheric air temperature biases. These issues are considered as the main sources of SEB uncertainty over ice‐free areas of the Arctic Ocean. The results from a coupled RCM imply that the SEB is constrained by both the atmosphere and the ocean (and sea ice) with considerable feedback. Coordinated improvement of both stand‐alone atmospheric and coupled RCMs would promote a more comprehensive and improved understanding of the Arctic air‐ice‐sea coupled system.

Inoue, Jun↗
DOE OSTI2022

Parameterizing Convective Organization Effects With a Moisture-PDF Approach in Climate Models: Concept and a Regional Case Simulation

We propose a parameterization scheme of convective organization effects based on a moisture-distribution approach, which can reflect aggregation of convective cells within a model grid as well as the interaction between convection and spatial heterogeneity in free-troposphere moisture. With this concept, convective cells in an aggregated state are surrounded by air that is moister than the grid-mean condition, which provides a “shielding” effect favorable for deeper convective updrafts. Such effects are represented quantitatively via utilizing a schematic diagram about the geometric interpretation of convective cluster, dry area, and their contact area, in which both the convective cluster size and dry area fraction increase during the development of convection. Our sensitivity analyses indicate that the new scheme performs well in capturing the mean precipitation features. Excluding the convective organization effects in the model leads to a considerable reduction in the simulated precipitation magnitude. Notably, there exists an inverse relationship between background mean moisture condition and sub-grid moisture variability for a given rain rate, suggesting that with a high spatial heterogeneity in free-troposphere moisture, convection can still develop and maintain its strength under relatively dry background conditions, consistent with the convection-permitting model simulation. Overall, our new parameterization of convective organization effects can successfully reproduce the relationship between precipitation and sub-grid moisture variability, which is a missing element in traditional convection parameterization schemes but important for the simulations of precipitation variability at various scales in climate models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Accurate assessment of land–atmosphere coupling in climate models requires high-frequency data output

Land–atmosphere (L–A) interactions are important for understanding convective processes, climate feedbacks, the development and perpetuation of droughts, heatwaves, pluvials, and other land-centered climate anomalies. Local L–A coupling (LoCo) metrics capture relevant L–A processes, highlighting the impact of soil and vegetation states on surface flux partitioning and the impact of surface fluxes on boundary layer (BL) growth and development and the entrainment of air above the BL. A primary goal of the Climate Process Team in the Coupling Land and Atmospheric Subgrid Parameterizations (CLASP) project is parameterizing and characterizing the impact of subgrid heterogeneity in global and regional Earth system models (ESMs) to improve the connection between land and atmospheric states and processes. A critical step in achieving that aim is the incorporation of L–A metrics, especially LoCo metrics, into climate model diagnostic process streams. However, because land–atmosphere interactions span timescales of minutes (e.g., turbulent fluxes), hours (e.g., BL growth and decay), days (e.g., soil moisture memory), and seasons (e.g., variability in behavioral regimes between soil moisture and latent heat flux), with multiple processes of interest happening in different geographic regions at different times of year, there is not a single metric that captures all the modes, means, and methods of interaction between the land and the atmosphere. And while monthly means of most of the LoCo-relevant variables are routinely saved from ESM simulations, data storage constraints typically preclude routine archival of the hourly data that would enable the calculation of all LoCo metrics. Here, we outline a reasonable data request that would allow for adequate characterization of sub-daily coupling processes between the land and the atmosphere, preserving enough sub-daily output to describe, analyze, and better understand L–A coupling in modern climate models. A secondary request involves embedding calculations within the models to determine mean properties in and above the BL to further improve characterization of model behavior. Higher-frequency model output will (i) allow for more direct comparison with observational field campaigns on process-relevant timescales, (ii) enable demonstration of inter-model spread in L–A coupling processes, and (iii) aid in targeted identification of sources of deficiencies and opportunities for improvement of the models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Using the GoAmazon-CHUVA measurements to understand what causes the biases in the onset of the rainy season in Amazonia in climate models

The onset of the Amazon rainy season has a large temporal and spatial variability, and strong impact on aerosols, ecosystems, fire, carbon fluxes, dry season length, agriculture, and hydropower. Two major droughts occurred in the region in the last 20 years, and the dry season has increased in length by about one month. These events highlight the urgency for improving our understanding and capability to project the rainy season onset and drought variability. However, global climate models (CMIP3 and CMIP5) appear to underestimate the past variability, and also project virtually no future change of the onset of rainy season over the Amazon even when they are forced by strong greenhouse forcing under the RCP8.5 scenario. Why these models underestimate the variability of the rainy season onset, and whether this bias implies an underestimate of sensitivity of their dry season length to anthropogenic radiative forcing remain unclear.

54 ENVIRONMENTAL SCIENCES↗