Improved knowledge of the cryosphere state is paramount for continued model development and for accurate estimates of fresh water supply. This work focuses on evaluation and potential improvements of current snow datasets over the United States. Snow in mountainous terrain is most difficult to quantify due to the slope, aspect, and remote nature of the environment. Due to the difficulty of measuring snow quantities in the mountains, the initial study creates a new method to upscale point measurements to area averages for comparison to initial snow quantities in numerical weather prediction models. The new method is robust and cross validation of the method results in a relatively low mean absolute error of 18% for snow depth (SD). Operational models at the National Centers for Environmental Prediction which use Air Force Weather Agency (AFWA) snow depth data for initialization were found to underestimate snow depth by 77% on average. Larger error is observed in areas that are more mountainous. Additionally, SD data from the Canadian Meteorological Center, which is used for some model evaluations, performed similarly to models initialized with AFWA data. The use of constant snow density for snow water equivalent (SWE) initialization for models which utilize AFWA data exacerbates poor SD performance with dismal SWE estimates. A remedy for the constant snow density utilized in NCEP snow initializations is presented in the next study which creates a new snow density parameterization (SNODEN). SNODEN is evaluated against observations and performance is compared with offline land surface models from the National Land Data Assimilation System (NLDAS) as well as the Snow Data Assimilation System (SNODAS). SNODEN has less error overall and reproduces the temporal evolution of snow density better than all evaluated products. SNODEN is also able to estimate snow density for up to 10 snow layers which may be useful for land surface models as well as conversion of remotely-sensed SD to SWE. Due to the poor performance of previously evaluated snow products, the last study evaluates openly-available remotely-sensed snow datasets to better understand the strengths and weaknesses of current global SWE datasets. A new SWE dataset developed at the University of Arizona is used for evaluation. While the UA SWE data has already been stringently evaluated, confidence is further increased by favorable comparison of UA snow cover, created from UA SWE, with multiple snow cover extent products. Poor performance of remotely-sensed SWE is still evident even in products which combine ground observations with remotely-sensed data. Grid boxes that are predominantly tree covered have a mean absolute difference up to 87% of mean SWE and SWE less than 5 cm is routinely overestimated by 100% or more. Additionally, snow covered area derived from global SWE datasets have mean absolute errors of 20%-154% of mean snow covered area.

As climate has warmed over recent years, a new pattern of more frequent and more intense weather events has unfolded across the globe. Climate models simulate such changes in extreme events, and some of the reasons for the changes are well understood. Warming increases the likelihood of extremely hot days and nights, favors increased atmospheric moisture that may result in more frequent heavy rainfall and snowfall, and leads to evaporation that can exacerbate droughts. Even with evidence of these broad trends, scientists cautioned in the past that individual weather events couldn't be attributed to climate change. Now, with advances in understanding the climate science behind extreme events and the science of extreme event attribution, such blanket statements may not be accurate. The relatively young science of extreme event attribution seeks to tease out the influence of human-cause climate change from other factors, such as natural sources of variability like El Niño, as contributors to individual extreme events. Event attribution can answer questions about how much climate change influenced the probability or intensity of a specific type of weather event. As event attribution capabilities improve, they could help inform choices about assessing and managing risk, and in guiding climate adaptation strategies. This report examines the current state of science of extreme weather attribution, and identifies ways to move the science forward to improve attribution capabilities.

Summarizes the science of climate change and impacts on the United States, for the public and policymakers.