Intensification mechanism analysis and anthropogenic climate change signal identification method for terrestrial water cycle (TWC) in dry and wet regions

12174,337

18/646784

April 26, 2024

An intensification mechanism analysis and anthropogenic climate change signal identification method for terrestrial water cycle (TWC) in dry and wet regions, includes: identifying dry regions and wet regions worldwide with observed data; quantifying a precipitation increasing rate; calculating a regional warming rate and a precipitation response warming rate, and investigating a difference between dry regions and wet regions; identifying a fingerprint pattern of a precipitation increase in dry regions and wet regions in response to global warming, and calculating a signal-to-noise ratio (SNR) to quantify a possibility of an anthropogenic climate change signal in dry regions and wet regions; and detecting different external forcing signals in a precipitation change of dry regions and wet regions with an optimal fingerprinting method, and quantifying a contribution of each of the different external forcing signals to the precipitation change.

CHINA UNIVERSITY OF GEOSCIENCES (WUHAN) (Wuhan, CN)

CHINA UNIVERSITY OF GEOSCIENCES (WUHAN) (Wuhan, CN)

1. An intensification mechanism analysis and anthropogenic climate change signal identification method for a terrestrial water cycle (TWC) in dry and wet regions, comprising the following steps: S 1 , acquiring climatic data, the climatic data comprising global gridded precipitation observation data, and simulated data in coupled model intercomparison project phase 6 (CMIP6) simulations, and the simulated data comprising precipitation data, near-surface air temperature data, meridional wind velocity data, zonal wind velocity data, specific humidity data and surface air pressure data; S 2 , calculating annual total precipitations (PRCPTOTs) across a global land surface according to the precipitation observation data, calculating an average annual PRCPTOT during a climatological period, and selecting top a % of grid cells as wet regions, and bottom a % of grid cells as dry regions, a being a preset value; S 3 , calculating an annual PRCPTOT in the dry regions and an annual PRCPTOT in the wet regions according to the precipitation observation data, the grid cells in the wet regions and the grid cells in the dry regions; and calculating an average annual PRCPTOT in the dry regions and the wet regions during the climatological period, performing normalization and regional averaging to obtain a normalized annual PRCPTOT, and quantifying a temporal trend of the normalized annual PRCPTOT in the dry regions and the wet regions to obtain a precipitation increasing rate in the dry regions and the wet regions; S 4 , calculating, according to the precipitation observation data and the near-surface air temperature data in the CMIP6 simulations, a weighted-area-averaged annual air temperature anomaly in the dry regions and a weighted-area-averaged annual air temperature anomaly in the wet regions, and quantifying a temporal trend of the air temperature anomaly in the dry regions and the wet regions to obtain a regional warming rate in the dry regions and the wet regions; and comparing the precipitation increasing rate in the dry regions and the wet regions obtained in the step S 3 with the regional warming rate in the dry regions and the wet regions to obtain a precipitation response warming rate in the dry regions and the wet regions; S 5 , calculating, according to the simulated data in the CMIP6 simulations, a weighted-area-averaged normalized vertically integrated water vapor (VIWV) and a weighted-area-averaged normalized vertically integrated water vapor transport (IWVT) in the dry regions and the wet regions in a historical period and a future period, and calculating a projected change of the future period compared with the historical period to obtain a VIWV increasing rate and an IWVT increasing rate in the dry regions and the wet regions in response to global warming; S 6 , performing, in combination with the normalized annual PRCPTOT in the step S 3 , rotated empirical orthogonal function (REOF) decomposition on three-dimensional (3D) spatio-temporal data of the annual PRCPTOT for the grid cells of the dry regions and 3D spatio-temporal data of the annual PRCPTOT for the grid cells of the wet regions to obtain a fingerprint pattern, projecting the fingerprint pattern to an observed and simulated annual PRCPTOT of the dry regions and the wet regions, and quantifying a signal-to-noise ratio (SNR) to calculate a significance of an anthropogenic climate change signal in the dry regions and the wet regions, wherein the step S 6 specifically comprises: S 61 , selecting 3D spatio-temporal data of the normalized annual PRCPTOT in the dry regions and the wet regions from a historical climate forcing (ALL) and future different Shared Socio-economic Pathway (SSP) simulations in the CMIP6, performing the REOF decomposition to obtain a spatial fingerprint pattern of a leading mode, and projecting the spatial fingerprint pattern to an observed annual PRCPTOT and a pre-industrial simulated annual PRCPTOT in the dry regions and the wet regions, with a projection being calculated by: [mathematical expression included] wherein, t represents a year, i represents a longitude of the grid cell, j represents a latitude of the grid cell, A represents an area of the grid cell, S represents the dry regions or the wet regions, PRCP represents the annual PRCPTOT, F represents the spatial fingerprint pattern, and lon s and lat s respectively represent a longitude set and a latitude set; S 62 , calculating a trend with the projection, calculating the SNR with an l-year trend projected by observed data and CMIP6 historical simulated annual PRCPTOT in the dry regions and the wet regions as a signal, and a standard deviation of an l-year trend projected by the CMIP6 pre-industrial simulated annual PRCPTOT as a noise, and dividing the significance of the signal according to the SNR, l being a preset value; and S 63 , calculating, from a m-year length to a n-year length, a projection trend for the CMIP6 historical simulated annual PRCPTOT and the CMIP6 pre-industrial simulated annual PRCPTOT in the dry regions and the wet regions, calculating time of emergence of the anthropogenic climate change signal according to the SNR, and validating a robustness of the anthropogenic climate change signal in an observed precipitation of the dry regions and the wet regions according to a model result, m and n being a preset value; and S 7 , performing, in combination with the precipitation data of the CMIP6 simulations obtained in the step S 1 , detection and attribution on an external forcing with a single-signal optimal fingerprinting method and a two-signal optimal fingerprinting method.

2. The intensification mechanism analysis and anthropogenic climate change signal identification method for the TWC in the dry and wet regions according to claim 1 , wherein in the step S 1 , the global gridded precipitation observation data or the precipitation data in the CMIP6 simulations is data obtained by ensemble averaging on multiple sets or pieces of precipitation data in different models.

3. The intensification mechanism analysis and anthropogenic climate change signal identification method for TWC in the dry and wet regions according to claim 1 , wherein in the step S 2 , the annual PRCPTOTs are calculated with a monthly precipitation or a daily precipitation; the average annual PRCPTOT is calculated according to the climatological period; average annual PRCPTOTs during the climatological period across grid cells of the global land surface are sorted; top 30% of grid cells are selected as the wet regions, and bottom 30% of grid cells are selected as the dry regions.

4. The intensification mechanism analysis and anthropogenic climate change signal identification method for the TWC in the dry and wet regions according to claim 1 , wherein the step S 3 specifically comprises: S 31 , selecting multiple sets of observed precipitation data from different models in different scenarios of the CMIP6, comprising the ALL, a greenhouse-gas forcing (GHG), an anthropogenic-aerosol forcing (AER), a natural forcing (NAT) and four future different SSP scenarios; and S 32 , calculating the average annual PRCPTOT during the climatological period, calculating a ratio of a difference between the annual PRCPTOT and the average annual PRCPTOT to the average annual PRCPTOT to obtain the normalized annual PRCPTOT, performing area-weighted averaging on the normalized annual PRCPTOT and the corresponding grid cells of the dry regions and the wet regions to obtain a weighted-area-averaged normalized annual PRCPTOT in the dry regions and the wet regions, quantifying the temporal trend of the normalized annual PRCPTOT in the dry regions and the wet regions to obtain the precipitation increasing rate in the dry regions and wet regions.

5. The intensification mechanism analysis and anthropogenic climate change signal identification method for TWC in the dry and wet regions according to claim 1 , wherein the step S 4 specifically comprises: S 41 , selecting the near-surface air temperature data from different scenarios in the CMIP6 simulations, comprising ALL, GHG, AER, NAT and four future different SSP scenarios; and S 42 , calculating an annual air temperature in the dry regions and an annual air temperature in the wet regions, calculating a climatological annual air temperature, quantifying a difference between the annual air temperature and the climatological annual air temperature to obtain the annual air temperature anomaly, performing weighted averaging on the annual air temperature anomaly and the corresponding grid cells of the dry regions and the wet regions to obtain a regionally averaged annual air temperature anomaly in the dry regions and wet regions, quantifying a trend to obtain a regional warming trend in the dry regions and wet regions, comparing the precipitation increasing rate with the regional warming rate to obtain the precipitation response warming rate in the dry regions and wet regions.

6. The intensification mechanism analysis and anthropogenic climate change signal identification method for the TWC in the dry and wet regions according to claim 1 , wherein the step S 5 specifically comprises: S 51 , selecting data from the ALL and future different SSP scenarios based on CMIP6, calculating the VIWV, the IWVT and a globally averaged annual air temperature, wherein the VIWV and the IWVT are respectively calculated by: [mathematical expression included] [mathematical expression included] wherein, ρ represents a water density, g represents a gravitational acceleration, p t represents a pressure at a top of an atmosphere, p s represents a near-surface pressure, q represents a specific humidity, v represents a meridional wind, and u represents a zonal wind; and S 52 , normalizing the VIWV and the IWVT same as the annual PRCPTOT, and calculating, in combination with the globally averaged annual air temperature, the VIWV and the IWVT of the dry regions and wet regions during the historical period and at an end of a twenty-first century in response to global warming.

7. The intensification mechanism analysis and anthropogenic climate change signal identification method for the TWC in the dry and wet regions according to claim 1 , wherein the optimal fingerprinting method in the step S 7 is given by: y =(X −α)β+∈ wherein, y represents an observed annual PRCPTOT time series of the dry regions and wet regions, X represents a simulated annual PRCPTOT time series of the dry regions and wet regions, comprising ALL, GHG, AER and NAT, β represents a scaling factor, and ∈ represents a regression residual; and the external forcing attributed by an observed change of the annual PRCPTOT in the dry regions and wet regions is quantified as: Con=Slope×β wherein, slope represents a simulated linear trend for the annual PRCPTOT in the dry regions and wet regions under each external forcing.

8. A storage device, wherein the storage device stores an instruction and data for realizing the intensification mechanism analysis and anthropogenic climate change signal identification method for a terrestrial water cycle (TWC) in dry and wet regions according to claim 1 .

9. An intensification mechanism analysis and anthropogenic climate change signal identification device for a terrestrial water cycle (TWC) in dry and wet regions, comprising a processor and a storage device, wherein the processor loads and executes an instruction and data in the storage device to realize the intensification mechanism analysis and anthropogenic climate change signal identification method for the TWC in the dry and wet regions according to claim 1 .

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