CLIMBra - Climate Change Dataset for Brazil

Abstract

General Circulation and Earth System Models are the most advanced tools for investigating climate responses to future scenarios of greenhouse gas emissions, playing the role of projecting the climate throughout the century. Nevertheless, climate projections are model-dependent and may show systematic biases, requiring a bias correction for any further application. Here, we provide a dataset based on an ensemble of 19 bias-corrected CMIP6 climate models projections for the Brazilian territory based on the SSP2-4.5 and SSP5-8.5 scenarios. We used the Quantile Delta Mapping approach to bias-correct daily time-series of precipitation, maximum and minimum temperature, solar net radiation, near-surface wind speed, and relative humidity. The bias-corrected dataset is available for both historical (1980-2013) and future (2015-2100) simulations at a 0.25° × 0.25° spatial resolution. Besides the gridded product, we provide area-averaged projections for 735 catchments included in the Catchments Attributes for Brazil (CABra) dataset. The dataset provides important variables commonly used in environmental and hydroclimatological studies, paving the way for the development of high-quality research on climate change impacts in Brazil.

Fig. 1

Flowchart representing the core steps used to generate the CLIMBra’s products. Step 1 and 2 represent the regrid and bias-correction tasks, respectively. Step 3 represents the framework required to rescaled the gridded dataset to the CABra’s catchments.

Fig. 2

(a) Streamflow gauge coordinates of CABra’s catchments, colored according to their mean elevation and sized by their area. (b) Histogram of catchments’ area. (c) Distribution of catchments per Brazilian biome. (d) Six main Brazilian biomes.

Fig. 3

Biases in long-term mean precipitation, maximum and minimum temperature, net shortwave surface radiation, relative humidity, and near surface wind speed considering the gridded dataset in both raw and bias-corrected conditions. The limits of Brazilian biomes are indicated in black borderlines.

Fig. 4

Biases in the long-term mean and extreme values of precipitation, maximum and minimum temperature, net shortwave surface radiation, relative humidity, and near surface wind speed (catchment-scale dataset) for the raw simulations. Histograms in each of the panels indicate the frequency of occurrence of bias.

Fig. 5

Biases in the long-term mean and extreme values of precipitation, maximum and minimum temperature, net shortwave surface radiation, relative humidity, and near surface wind speed (catchment-scale dataset) for the bias-corrected simulations. Histograms in each of the panels indicate the frequency of occurrence of bias.

Fig. 6

Long-term (1980–2013) monthly mean of precipitation and maximum and minimum temperature in each Brazilian biome. Highlighted lines represent the intra-annual cycle simulated by the raw multi-model ensemble. Dashed lines indicate the observed mean intra-annual cycle. Confidence intervals represent the maximum and minimum values simulated by the raw 19 CMIP6 GCMs/ESMs.

Fig. 7

Long-term (1980–2013) monthly mean of precipitation and maximum and minimum temperature in each Brazilian biome. Highlighted lines represent the intra-annual cycle simulated by the bias-corrected multi-model ensemble. Dashed lines indicate the observed mean intra-annual cycle. Confidence intervals represent the maximum and minimum values simulated by the bias-corrected 19 CMIP6 GCMs/ESMs.

Fig. 8

Relative changes in the long-term mean and extreme values of precipitation, maximum and minimum temperature, net shortwave solar radiation, relative humidity, and near surface wind speed between the historical (1980–2013) and distant future (2070–2100; SSP2-4.5) periods (bias-corrected catchment-scale dataset). Histograms in each panel indicate the frequency of occurrence of relative changes.

Fig. 9

Relative changes in the long-term mean and extreme values of precipitation, maximum and minimum temperature, net shortwave solar radiation, relative humidity, and near surface wind speed between the historical period (1980–2013) and the distant future (2070–2100; SSP5-8.5) (bias-corrected catchment-scale dataset). Histograms in each of the panels indicate the frequency of occurrence of relative changes.

Fig. 10

Relative changes in the long-term mean intra-annual cycles of precipitation and maximum and minimum temperatures between the historical (1980–2013) and distant future (2070–2100, SSP5-8.5) periods. Highlighted lines represent the changes in the intra-annual cycle simulated by the bias-corrected multi-model ensemble.