Search

It has been demonstrated that the Arctic has warmed at almost four times the global average rate since 1979, a phenomenon known as Arctic amplification. However, this rapid Arctic warming is tightly linked to the retreat and thinning of summer sea ice, and so may be expected to weaken as the Arctic transitions to seasonal ice cover. Here we show evidence from gridded observations and climate reanalysis that Arctic amplification peaked sometime in the early 2000s. This occurred concurrently with a maximum in the rate of loss of sea ice area, thickness, and volume. From CMIP6 projections and the CESM2 large ensemble we see that Arctic amplification is unlikely to be so high again at any future point in the 21st century except in the lowest emissions scenarios in which global temperatures stabilize while the Arctic continues to warm.

Advances in climate science have rendered obsolete the gridded observation data widely used in downstream applications. Novel climate reanalysis products outperform legacy data products in accuracy, temporal resolution, and provision of uncertainty metrics. Consequently, there is an urgent need to develop a workflow through which to integrate these improved data into biological analyses. The ERA5 product family (ERA5 and ERA5-Land) are the latest and most advanced global reanalysis products created by the European Center for Medium-range Weather Forecasting. These data products offer up to 83 essential climate variables at hourly intervals for the time-period of 1981 to today with preliminary back-extensions being available for 1950–1981. Spatial resolutions range from 30 × 30 km (ERA5) to 11 × 11 km (ERA5-Land) and can be statistically downscaled to study-requirements at finer spatial resolutions. Kriging is one such method to interpolate data to finer resolutions and has the advantages that one can leverage additional covariate information and obtain the uncertainty associated with the downscaling. The KrigR R-package enables users to (a) download ERA5(-Land) climate reanalysis data for a user-specified region, and time-period, (b) aggregate these climate products to desired temporal resolutions and metrics, (c) acquire topographical co-variates, and (d) statistically downscale spatial data to a user-specified resolution using co-variate data via kriging. KrigR can execute all these tasks in a single function call, thus enabling the user to obtain any of 83 (ERA5)/50 (ERA5-Land) climate variables at high spatial and temporal resolution with a single R-command. Additionally, KrigR contains functionality for computation of bioclimatic variables and aggregate metrics from the variables offered by ERA5(-Land). This R-package provides an easy-to-implement workflow for implementation of state-of-the-art climate data while avoiding issues of storage limitations at high temporal and spatial resolutions by providing data according to user-needs rather than in global data sets. Consequently, KrigR provides a toolbox to obtain a wide range of tailored climate data at unprecedented combinations of high temporal and spatial resolutions thus enabling the use of world-leading climate data through the R-interface and beyond.

There is an increasing need for high spatial and temporal resolution climate data for the wide community of researchers interested in climate change and its consequences. Currently, there is a large mismatch between the spatial resolutions of global climate model and reanalysis datasets (at best around 0.25° and 0.1° respectively) and the resolutions needed by many end-users of these datasets, which are typically on the scale of 30 arcseconds (∼900 m). This need for improved spatial resolution in climate datasets has motivated several groups to statistically downscale various combinations of observational or reanalysis datasets. However, the variety of downscaling methods and inputs used makes it difficult to reconcile the resultant differences between these high-resolution datasets. Here we make use of the KrigR R-package to statistically downscale the world-leading ERA5(-Land) reanalysis data using kriging. We show that kriging can accurately recover spatial heterogeneity of climate data given strong relationships with co-variates; that by preserving the uncertainty associated with the statistical downscaling, one can investigate and account for confidence in high-resolution climate data; and that the statistical uncertainty provided by KrigR can explain much of the difference between widely used high resolution climate datasets (CHELSA, TerraClimate, and WorldClim2) depending on variable, timescale, and region. This demonstrates the advantages of using KrigR to generate customized high spatial and/or temporal resolution climate data.

The Earth has warmed in the last century with the most rapid warming occurring near the surface in the Arctic. This Arctic amplification occurs partly because the extra heat is trapped in a thin layer of air near the surface due to the persistent stable-stratification found in this region. The amount of warming depends upon the extent of turbulent mixing in the atmosphere, which is described by the depth of the atmospheric boundary layer (ABL). Global climate models (GCMs) tend to over-estimate the depth of stably-stratified ABLs, and here we show that GCM biases in the ABL depth are strongly correlated with biases in the surface temperature variability. This highlights the need for a better description of the stably-stratified ABL in GCMs in order to constrain the current uncertainty in climate variability and projections of climate change in the surface layer.

There are a number of asymmetries in the surface air temperature response to forcing, including polar amplification and changes to the diurnal and seasonal temperature ranges. We propose that such spatial–temporal signatures of climate change can, in part, be explained by differences in the effective heat capacity of the atmosphere. We have demonstrated that predictions arising from this hypothesis are simultaneously satisfied through the analysis of temperature records from daily to inter-decadal timescales using observational and reanalysis datasets. This mechanism can help to explain why we see the largest temperature trends in the winter months (0.42 K/decade in winter compared to 0.18 K/decade in summer) and why the diurnal temperature range decreases in a warming world, having decreased by ∼0.4 K since 1950.