Data and Methods
A set of high resolution, bias-corrected climate change projections that can be used to evaluate climate change impacts on processes that are sensitive to finer-scale climate gradients and the effects of local topography on climate conditions.
The Intergovernmental Panel on Climate Change (IPCC) has made available a suite of global models under Coupled Model Intercomparison Project Phase 6 (CMIP6), which is the best tool to simulate global climate systems on various space-time scales. However, due to their coarse resolution, these models may not be able to capture the fine-scale regional details. This report proposes using a suite of statistically downscaled models over the Indian Sub-continent.
The new generation of the state-of-the-art GCM simulations produced by CMIP6 have been developed and released and are the most advanced datasets currently available for climate change studies. In this new phase of CMIP6, the climate modules are further incorporated and improved to better represent the Earth’s climate system. Dynamic improvements are added to simulate a more reasonable climate, especially many advanced GCMs, higher horizontal resolution, better representation of synoptic process, comprehensive scenario development, and up-to-date forcing data consideration (Eyring et al. 2016).
These simulations are based on GCMs, describing the complex physical processes within the climate system compared to the previous GCM versions. For getting reliable climate projections at a local or regional scale, high-resolution data plays an important role in formulating adaptation strategies (Haarsma et al., 2016). However, high-resolution simulations of GCMs or downscaled GCMs often have substantial biases in their simulations. Thus, bias correction techniques are used to rectify this problem (Teutschbein and Seibert 2012).
With this background, we used the statistically downscaled, bias-corrected, and high resolution (0.25° x 0.25°) daily precipitation, maximum and minimum temperature developed by Mishra et al. (2020a) over the Indian sub-continental for assessing the future changes. To develop the statistically downscaled and bias-corrected data, Mishra et al. (2020b) used the Empirical Quantile Mapping (EQM) for thirteen GCM simulations from CMIP6. This was done with the help of the transfer function. We can find more details on EQM from Cannon, (2011) and Trasher et al. (2012). Mishra et al. (2020b) used a single ensemble member for each model for fair comparisons, typically the first ensemble member (r1i1p1f1 from CMIP6) to prepare these data sets.
The historical run period is from 1951 to 2014 for each experiment, and future projections from 2015 to 2100 forced SSP 2−4.5 (mid-range emissions) and SSP 5−8.5 (high-end emissions) scenarios. The details of the models used in the present report are given in Table 1.
Here, we used both SSP 2−4.5 and SSP 5−8.5 scenarios data for understanding future precipitation and temperature changes which are the combined Shared Socioeconomic Pathways (SSP) and target radiative forcing levels at the end of the 21st century from CMIP6, as these are the most promising scenarios at which the present climate behaves. The purpose of these datasets is to provide a set of high resolution, bias-corrected climate change projections that can be used to evaluate climate change impacts on processes that are sensitive to finer-scale climate gradients and the effects of local topography on climate conditions.
Also, we analysed 13 CMIP6 GCMs of monthly mean relative humidity (RH) data in this work, considering all the parallel to the temperatures available. RH data on each model’s original grids were interpolated linearly onto a standard grid resolution (1°×1°) to calculate the multimodel mean (MMM).
This report ought to compare the model datasets with the existing observational datasets to verify the agreement among the datasets. The necessity of the comparison is to understand to what extent these model datasets used in the present report can capture the actual scenarios. Many authors reported the differences between the model simulations and globally observational based satellite and reanalysis datasets.
In the present report, we chose the precipitation and temperature datasets developed mainly based on the observational network over India compared with the Multi Model Mean (MMM) of CMIP6. We compared the model simulations with high-resolution gridded datasets prepared by India Meteorological Department for both daily precipitation and temperature.
The daily precipitation dataset (grid resolution: 0.25° × 0.25°), maximum, minimum and mean temperature datasets (grid resolution: 1°×1°) developed by the India Meteorological Department (IMD) have been used to compare with the CMIP6 model simulations. These observations provide reliable data subject to the quality control process, which has been developed after following quality control of the station data and more details of the development of the data are available in Srivastava et al. (2009). However, to reduce the uncertainty, the IMD outputs were brought into a common resolution of 0.25°×0.25° using the bilinear interpolation technique to allow the evaluations to be made at common resolution.
To assess consistency among the CMIP6 models, we have divided the 21st century into four different periods. The historical simulation period 1951 – 1970 is selected as the ‘baseline period’ and for future climate projections, we used the three future time slices 2021 – 2040, 2051 – 2070, and 2081 – 2100, which are treated as a near (2030s), mid (2060s), and far future (2090s) under the two emission scenarios of SSP 2−4.5 and SSP 5−8.5. In the present report, all the model simulations and observations have been maintained to a uniform grid resolution of 0.25°×0.25° for making them consistent for computing the Multi-Model Mean (MMM). It will help the report to bring out the precise diagnosis of precipitation and temperature changes on a space and time scale in the present and future climate.
This report examines the state and district levels of present and future changes in mean and extreme precipitation characteristics during southwest (JJAS) and northeast (OND) monsoon seasons over the Indian sub-continent. Also, this report examined the temperature characteristics on state and district levels of present and future changes during summer (MAM) and winter (DJF) seasons over the Indian sub-continent in the near, mid, and far future relative to the baseline period (1951−1970).
We used FIVE key indices for precipitation and SIX key indices for maximum and minimum temperatures defined by the Expert Team on Climate Change Detection and Indices (ETCCDI) for the present analysis (Zhang et al. 2011). All the details for these indices are given in Table 2.a and 2.b. All the indices mentioned in this report have been calculated for each model and then calculated the Multi-Model Mean (MMM) on a seasonal basis for the baseline and future scenarios till end of the 21st century, respectively.
Table 1 : Information of 13 CMIP6 climate models
| Model Name | Coupled Model Name (Country) | GCM Resolution |
|---|---|---|
| ACCESS-CM2 | Australian Community Climate and Earth System Simulator-Climate Model 2 (Australia) | 1.9°×1.3° |
| ACCESS-ESM1‑5 | Australian Community Climate and Earth System Simulator-Earth System Model 1.5 (Australia) | 1.9°×1.2° |
| BCC-CSM2-MR | Beijing Climate Center, Climate System Model 2 (MR) (China) | 1.1°×1.1° |
| CanESM5 | Canadian Earth System Model 5 (Canada) | 2.8°×2.8° |
| EC-Earth3 | European Centre Earth Consortium model 3 (Europe) | 0.7°×0.7° |
| EC-Earth3-Veg | European Centre Earth Consortium model 3 (Veg) | 0.7°×0.7° |
| INM-CM4‑8 | Institute for Numerical Mathematics Climate Model 4.8 (Russia) | 2°×1.5° |
| INM-CM5‑0 | Institute for Numerical Mathematics Climate Model 5 (Russia) | 2°×1.5° |
| MPI-ESM1-2-HR | Max Planck Institute for Meteorology Earth System Model 1.2 (HR) (Germany) | 0.9°×0.9° |
| MPI-ESM1-2-LR | Max Planck Institute for Meteorology Earth System Model 1.2 (LR) (Germany) | 1.9°×1.9° |
| MRI-ESM2‑0 | Meteorological Research Institute Earth System Model 2.0 (Japan) | 1.1°×1.1° |
| NorESM2-LM | Norwegian Earth System Model 2 (LM) (Norway) | 2.5°×1.9° |
| NorESM2-MM | Norwegian Earth System Model 5 (MM) (Norway) | 0.9°×1.3° |
Table 2.a : Climate variables used for these projections | Precipitation Indices
| Name | Definition (in Units) |
|---|---|
| Rainy Day | A day with rainfall amount more than 2.5 mm (in days) |
| Simple daily intensity | Ratio of seasonal total precipitation to the number of days with precipitation ≥ 1 mm (in mm/day) |
| Maximum 1‑day precipitation | Seasonal maximum 1‑day precipitation amount (in mm) |
| Highest consecutive 5‑day precipitation | Seasonal maximum of total precipitation accumulated over any consecutive 5‑day period (in mm) |
| Consecutive 5‑day precipitation events | Number of separate 5‑day periods where the total precipitation exceeds 50 mm (in number of events) |
| Heavy precipitation days | Number of days with precipitation greater than 10 mm (in days) |
| Very heavy precipitation days | Number of days with precipitation greater than 25 mm (in days) |
| Consecutive dry days | Longest stretch within the season of consecutive dry days with daily precipitation < 1 mm (in days) |
| Consecutive dry day events | Number of separate periods with more than 5 consecutive days where daily precipitation is < 1 mm (in number of events) |
Table 2.b : Climate variables used for these projections | Temperature Indices
| Name | Definition (in Units) |
|---|---|
| Summer Days | Seasonal count of days when daily maximum temperature is greater than 30°C (in days) |
| Consecutive Summer Days | Longest seasonal stretch of consecutive days when the daily maximum temperature is above 30°C (in days) |
| Consecutive Summer Day Events | Number of distinct periods of consecutive days where the daily maximum temperature remains above 30°C for more than 5 days (in number of events) |
| Heat wave Duration Index (number of heatwave days) | Longest period within the season with at least five consecutive days where the maximum daily temperature exceeds the 1951 – 1970 mean plus 5°C (in days) |
| Heat wave Duration Index (number of heatwave events) | Count of separate heatwave events, where each event consists of at least five consecutive days with maximum daily temperature exceeding the 1951 – 1970 mean plus 5°C (in number of events) |
| Heat wave Frequency Index (Number of Warm Spell Days) | Longest period within the season of at least five consecutive days where the daily average temperature is above the 1951 – 1970 reference period’s 90th percentile of daily mean temperature (in days) |
| Heat wave Frequency Index (Number of Warm Spell Events) | Number of separate warm spell events, with each event consisting of at least five consecutive days where the daily average temperature is above the 1951 – 1970 reference period’s 90th percentile of daily mean temperature (in number of events) |
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