Impact of Climate Change on Snowpack Trends in Northern Hemisphere River Basins. A Multi-Geometry Study of Alaska, Canada, Central Asia
In the Northern Hemisphere, the researchers only saw a minimal loss of water in 80 percent of the time. Parts of Alaska, Canada, and Central Asia even experienced increased snowpack. Eventually, though, if the planet keeps heating up, even those places could fall off the snow-loss cliff.
“Where the majority of people live and where the majority of people put increasingly competitive uses on water availability, particularly from snow — they live in places that are at or on this snow-loss cliff,” said Justin Mankin, an associate professor of geography at Dartmouth and senior author of the new research paper.
There is no longer a need for a short-term emergency after a basin falls off a cliff. Instead, they will be adapting to permanent changes to water availability,” Mankin said in a press release.
The methods detailed above yield 12,120 estimates of the effect of climate change on March snowpack trends in each of 169 major river basins. Contributing to the spread of these estimates are four main sources of uncertainty: (1) uncertainty in the SWE data products on which the reconstructions are based; (2) uncertainty in the temperature and precipitation data products and their relationship with SWE; (3) differences in the forced response of temperature and precipitation due to structural differences between climate models; and (4) uncertainty due to internal climate variability in temperature and precipitation.
“We were able to identify a really clear fingerprint of anthropogenic emissions,” says Alex Gottlieb, first author of the new study and a PhD student at Dartmouth. They could clearly see the impact pollution from fossil fuels had on snow trends in the Northern Hemisphere.
It’s been hard to make this connection until now because global warming leads to both higher temperatures and more precipitation, which can counteract each other. You can have a warmer average temperature but less snow in a storm.
“The study reveals a surprising nonlinear relationship between snow mass and temperature, which has complex ramifications,” Jouni Pulliainen, a research professor at the Finnish Meteorological Institute, writes in an accompanying article that comments on the new research.
Climate scientists say the study highlights the vulnerabilities of the region, including the lack of water, because of California’s dependence on both the Colorado River Basin and Sierra Nevada.
Gridded precipitation data come from the ECMWF’s ERA5 reanalysis53; the Global Precipitation Climatology Centre (GPCC)54; MERRA-247; Multi-Source Weighted-Ensemble Precipitation (MSWEP), Version 255; and TerraClimate49. Berkeley Earth is the data source for gridded temperature data. Global Unified Temperature57; ERA553; and MERRA-247. The daily gridded data is provided by the global flood awareness system. Details of all datasets used in the analysis are given in Extended Data Table 1.
To better understand the drivers of the heterogeneous spatial response of SWE and its potential future changes with further warming, we evaluate the temperature sensitivity of March SWE across a gradient of climatological winter temperatures in in situ observations, gridded observations, our basin-scale reconstructions and climate models. The marginal effect of an additional degree of warming, ∂SWE/∂T or β1, is calculated as the regression coefficient of March SWE on cold-season (November–March) temperature:
All estimates of basin population are calculated using the 2020 values from the 15 arcmin Gridded Population of the World, Version 4 (GPWv4) dataset from NASA’s Socioeconomic Data and Applications Center60.
Forecasting water level variability across a single hemisphere for all climate models using standard deviations across multiple datasets from 1981 to 2020, and in situ at the same time
To quantify the magnitude of uncertainty introduced by each source, we calculate the standard deviation of forced SWE trends across a single dimension, holding all others at their mean. For instance, the uncertainty due to differences in model structure is given by the standard deviation of forced SWE trends across the 12 climate models (considering only the first realization from each), taking the mean across all SWE–temperature–precipitation dataset combinations.
In water year March SWE is an average. The grid cell i has an average temperature in month m of water year y and the total precipitation in month m of water year y. We fit the model using the full spatiotemporal panel of 0.5° × 0.5° gridded data (that is, all grid-cell years from 1981 to 2020), then aggregate the predicted gridded values to the river-basin scale. There are two main advantages to training a single model on the full panel of data over training multiple models on more local data. First is that the out-of-sample prediction skill of the full panel model is significantly higher in many highly populated mid-latitude basins of the western USA, western Europe and High Mountain Asia; local models are more skilful in fewer than 20% of basins, concentrated in sparsely populated high-latitude basins where the skill of the full panel model is already high (Extended Data Fig. 3). A model on data from the entire hemisphere can give better statistical stability than a model on variables from one hemisphere. It could surpass the support of localhistorical observations as records fall.
As an additional test of model skill, we use the model trained on only the gridded observational products to predict fully out-of-sample March SWE at 2,961 in situ sites from the SNOTEL, CanSWE and NH-SWE datasets. Our reconstructions are able to capture the interannual variability in in situ SWE quite well, with a median R2 across stations of 0.59 and an RMSE of around 22% (Extended Data Fig. 5). The reconstruction model predictions are similarly able to capture skillfully the long-term SWE trends at the in situ sites, with a pattern correlation of 0.72 (Extended Data Fig. 5). Finally and crucially, we confirm that there are no systematic trends in time of the bias of our reconstructions against the in situ observations (Supplementary Fig. 5), indicating that the reconstruction models are capturing the real-world rate of change of snowpack with high fidelity.
where S is the uncertainty from SWE observations, TP is the uncertainty from temperature and precipitation observations, M is the uncertainty from model structure, and I is the uncertainty from internal variability. To determine which sources are the biggest contributors to uncertainty in the basin, we take the uncertainty of each source into account. This fractional uncertainty is reported in Supplementary Fig. 12. For each source, we will hatch out basins where uncertainty is not enough to change the sign of the mean estimate of the forced SWE trend.
Forced temperature and precipitation changes affect spring SWE, and the difference between historical sew and last spring sew is used to calculate the spring precipitation change.