Bias‐corrected data sets of climate model outputs at uniform space–time resolution for land surface modelling over Amazonia

Moghim, Sanaz; McKnight, S. L.; Zhang, Ke; Ebtehaj, Ardeshir M.; Knox, R. G.; Bras, Rafael L.; Moorcroft, P. R.; Wang, Jingfeng

doi:10.1002/joc.4728

Cited by 19 publications

(14 citation statements)

References 82 publications

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“…The performance of the GCMs (e.g., CCSM) to simulate temperature and precipitation varies over different regions (Murphy, ; Moghim et al , ). Biases in simulations can be caused by schemes and parameterizations used in the models that can be proper for some but not all domains.…”

Section: Methodsmentioning

confidence: 99%

“…Thus, biases at regions' pixels (validating pixels) could be corrected with a regional model trained at a limited number of the pixels (training pixels). The performance of the GCMs (e.g., CCSM) to simulate temperature and precipitation varies over different regions (Murphy, 1999;Moghim et al, 2016). Biases in simulations can be caused by schemes and parameterizations used in the models that can be proper for some but not all domains.…”

Section: Methodsmentioning

confidence: 99%

“…The performance of the CCSM to simulate varied climate variables like temperature and precipitation is different (Gleckler et al, 2008;Sillmann et al, 2013;Abbasian et al, 2018). Also, systematic errors in climate models are space and time dependent quantities (van Oldenborgh et al, 2005;de Szoeke and Xie, 2008;Moghim et al, 2016). Thus, the boundaries of the delineated regions based on the temperature and precipitation bias correction models can shift slightly for different evaluation times (months or seasons).…”

Section: Precipitationmentioning

confidence: 99%

“…The biased model outputs can significantly impair results of other models that use those outputs as drivers (e.g., Dai, ; ; ; Solomon et al , , among other studies). Thus, we need to correct the biases of climate model outputs, which are used as inputs into the terrestrial biosphere and hydrological models for climate studies (Zhang et al , ; Moghim et al , ). Due to underlying natural nonlinearities and complexities of the governing equations of the GCMs, intrinsic bias correction within the model structure may not be practically feasible.…”

Section: Introductionmentioning

confidence: 99%

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Regression‐based regionalization for bias correction of temperature and precipitation

Moghim

Bras

2019

Intl Journal of Climatology

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Statistical bias correction methods are inferred relationships between inputs and outputs. The constructed functions are based on available observations, which are limited in time and space. This study investigates the ability of regression models (linear and nonlinear) to regionalize a domain by defining a minimum number of training pixels necessary to achieve a good level of bias correction performance. Linear regression is used to divide northern South America into five regions. To correct the biases of temperature and precipitation, an artificial neural network (ANN) model was trained with selected pixels within each region and then used to reproduce bias‐corrected temperature and precipitation at all pixels within the delineated regions. The Community Climate System Model (CCSM) provided the climate model data. Results confirm that it is possible to identify regions in terms of physical features such as land cover, topography, and climatology over which models trained with a few pixels can correct the biases of climate variables with good accuracy over the entire domain. This approach saves computational time and reduces memory usage of using ANNs for correcting biases in climate model outputs.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Precipitationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Regression‐based regionalization for bias correction of temperature and precipitation

Moghim

Bras

2019

Intl Journal of Climatology

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“…One approach has been to downscale and bias correct the meteorological inputs, typically precipitation and temperature (e.g., Guimberteau et al, 2017;van Vliet et al, 2013). As discussed in Hashino et al (2007), several techniques are being applied to bias correct meteorological forcing data: simple approaches calculate a "delta factor" (e.g., Diaz-Nieto & Wilby, 2005), but other more sophisticated statistical approaches also exist (e.g., Fang et al, 2015;Moghim et al, 2016). In other cases, original General Circulation Model data have been used to force a regional climate model specifically calibrated for the domain under consideration (e.g., Jacob et al, 2007).…”

Section: Bias Correction Of Simulated Streamflowsmentioning

confidence: 99%

Future Climate and Land Use Change Impacts on River Flows in the Tapajós Basin in the Brazilian Amazon

et al. 2019

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Land conversion and changing climate are expected to significantly alter tropical forest hydrology. We used a land surface model integrated with a river routing scheme to analyze the hydrological alterations expected in the Tapajós River basin, a large portion of the Brazilian Amazon, caused by two environmental drivers: climate and land use. The model was forced with two future climate scenarios (years 2026-2045) from the Earth System Model HadGem2-ES with moderate (+4.5 W/m 2 radiative forcing value in the year 2100 with respect to preindustrial levels) and severe (+8.5 W/m 2 ) representative atmospheric carbon dioxide pathways (Representative Concentration Pathways). We tested the sensitivity of our results to the uncertainty in future climate projections by running simulations with IPSL-CM5 (wettest scenarios) and GISS-E2 (driest scenarios). Human land use effects on vegetation were evaluated using a limited and an extreme deforestation scenario. Our analysis indicates that climate change is predicted to reduce river flows across seasons (up to 20%) and bring a considerable shift in flow seasonality toward a later onset (nearly 1.5 months) and increase in interannual variability. While land use change partially counteracts the climate-driven diminishing trend in river flows, it is expected to contribute to a further increase in interannual and intraannual variability. From a water management perspective, the overall reduction of river flows and their increased variability, combined with the shift and the shortening of the wet season, could potentially affect the productivity of the large hydropower systems planned for the region and the growing demand for agricultural and transport expansion.Plain Language Summary Climate and land use change are expected to heavily modify the water cycle. This is particularly true in tropical areas, where human-driven changes may completely alter river discharge. The Amazon is the largest of the remaining tropical forests. Increasing agriculture and livestock production have significantly altered land cover in the region. This pattern is projected to continue in the coming decades. The change in the Amazon's future is highly uncertain: the direct effects of climate and land use change are not well understood and the response also depends on complex Earth-atmosphere exchanges. We used computer models representing water and energy cycles over time to understand how environmental changes are likely to alter the discharge of the Tapajós River system in southeastern Amazon. By combining two climate and two land use scenarios, we found that climate change is expected to reduce the river flows in the basin, bringing a delay in the flow seasonality and increasing the overall variability. Land use change, conversely, is expected to cause an increase in flow magnitude and variability. Besides the serious environmental consequences, these findings have important implications for the management and development of water in this strategic area for Brazil's energy and food production.

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Implications of CMIP6 projected drying trends for 21st century Amazonian drought risk

Parsons

2020

Preprint

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Recent exceptionally hot droughts in Amazonia have highlighted the potential role of global warming in driving changes in rainfall and temperatures in the region. The previous generation of global climate models projected that eastern Amazonia would receive less future precipitation while western Amazonia would receive more precipitation, but many of these models disagreed on future precipitation trends in the region. Here Coupled Modeling Intercomparison Project, Phase 6 (CMIP6) models are used to examine the shifting risk of eastern Amazonian droughts under high and low future greenhouse gas emissions scenarios. This new generation of models shows better agreement that most of the Amazonian basin will receive less future rainfall, with particularly strong agreement that eastern and southern Amazonia will dry in the 21st century. These models suggest that global warming may be increasing the likelihood of exceptionally hot drought in the region. With unabated global warming, recent particularly warm and severe droughts will become more common by midcentury, but reducing the rate of greenhouse gas emissions can make extremely hot and dry years less common in the future. Simulated future rainfall changes in Amazonia under high greenhouse gas emissions are associated with changes in the tropical Pacific, but many climate models struggle to reproduce observed trends in the tropical Pacific. These shortcomings highlight the need to improve confidence in global climate models' ability to simulate observed trends in the tropics, even if more CMIP6 models agree on the sign of future rainfall trends. Plain Language SummaryRecent exceptionally hot droughts in Amazonia have highlighted the potential role of global warming in driving changes in rainfall and temperatures in the region. The previous generation of global climate models projected that eastern Amazonia would receive less future rainfall while western Amazonia would receive more rainfall. Here the latest climate model simulations are used to examine future rainfall and temperature changes over tropical South America. The new generation of climate models shows that most of the Amazonian basin will receive less future rainfall, with particularly strong agreement that eastern and southern Amazonia will dry in the future if the planet continues to rapidly warm. These models suggest that global warming has already increased the likelihood of exceptionally hot drought in the region, and by midcentury under unabated global warming, recent particularly warm and severe droughts will become more common. Reducing future greenhouse gas emissions decreases these warming and drying trends but does not eliminate them. However, many climate models have traditionally struggled to reproduce several key climate features in the tropics.

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Bias‐corrected data sets of climate model outputs at uniform space–time resolution for land surface modelling over Amazonia

Cited by 19 publications

References 82 publications

Regression‐based regionalization for bias correction of temperature and precipitation

Regression‐based regionalization for bias correction of temperature and precipitation

Future Climate and Land Use Change Impacts on River Flows in the Tapajós Basin in the Brazilian Amazon

Implications of CMIP6 projected drying trends for 21st century Amazonian drought risk

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