Over the last decades, climate science has evolved rapidly across multiple expert domains. Our best tools to capture state-of-the-art knowledge in an internally self-consistent modelling framework are the increasingly complex fully coupled Earth System Models (ESMs). However, computational limitations and the structural rigidity of ESMs mean that the full range of uncertainties across multiple domains are difficult to capture with ESMs alone. The tools of choice are instead more computationally efficient reduced complexity models (RCMs), which are structurally flexible and can span the response dynamics across a range of domain-specific models and ESM experiments. Here we present Phase 2 of the Reduced Complexity Model Intercomparison Project (RCMIP Phase 2), the first comprehensive intercomparison of RCMs that are probabilistically calibrated with key benchmark ranges from specialised research communities. Unsurprisingly, but crucially, we find that models which have been constrained to reflect the key benchmarks better reflect the key benchmarks. Under the low-emissions SSP1-1.9 scenario, across the RCMs, median peak warming projections range from 1.3 to 1.7{degree sign}C (relative to 1850-1900, using an observationally-based historical warming estimate of 0.8{degree sign}C between 1850-1900 and 1995-2014). Further developing methodologies to constrain these projection uncertainties seems paramount given the international community’s goal to contain warming to below 1.5{degree sign}C above pre-industrial in the long-term. Our findings suggest that users of RCMs should carefully evaluate their RCM, specifically its skill against key benchmarks and consider the need to include projections benchmarks either from ESM results or other assessments to reduce divergence in future projections.

Changes in climate are usually considered in terms of trends or differences over time. However, for many impacts requiring adaptation, it is the amplitude of the change relative to the local amplitude of climate variability which is more relevant. Here, we develop the concept of ‘signal-to-noise’ in observations of local temperature, highlighting that many regions are already experiencing a climate which would be ‘unknown’ by late 19century standards. The emergence of observed temperature changes over both land and ocean is clearest in tropical regions, in contrast to the regions of largest change which are in the northern extra-tropics - broadly consistent with climate model simulations. Significant increases and decreases in rainfall have also already emerged in different regions with the UK experiencing a shift towards more extreme rainfall events, a signal which is emerging more clearly in some places than the changes in mean rainfall.

The Patagonian Icefields (Northern Patagonia Icefield and Southern Patagonia Icefield) are the largest ice bodies in the Southern Hemisphere outside Antarctica. Nonetheless, little is known about their main meteorological and glaciological features (mean state, variability and trends) in present climate (last ~30 years). The lack of temporarily and spatially dense observational data in this area has imposed a limitation on the assessment of the atmosphere-cryosphere interaction, a key issue for understanding the past, present and future evolution of these ice bodies and more generally, the southern Andes cryosphere. In this work, we overcome the absence of surface data by modeling the present-day atmospheric surface conditions for southern Andes. We first use a regional climate model (RegCMv4) to dynamically downscale the ERA-Interim reanalysis at 10-km spatial resolution for the period 1980-2015. Then, we statistically downscale its outputs to a 450-m resolution grid. This meteorological forcing is used later as an input for a simplified surface mass balance model. The surface mass balance output is analyzed particularly for spatial and temporal variability as well as trends. This allows us to have a better understanding of the local-scale atmospheric control (i.e., surface temperature, solar radiation and precipitation) over the surface mass balance of Patagonian Icefields. In order to assess the large-scale control over the surface mass balance of Patagonian Icefields, time series of spatially-averaged modeled fields are projected upon main atmospheric fields obtained from ERA-Interim reanalysis. Main results show that years of relatively high (low) surface mass balance are associated with low (high) pressure anomalies near the Bellingshausen Sea, causing an anomalous cyclonic (anticyclonic) circulation that enhances (reduces) the westerlies impinging the Patagonian Icefields which in turn increments (decrements) the surface mass balance. Only weak correlations between the mass balance and the main atmospheric modes of variability (ENSO, PDO, SAM) were found, suggesting little dependency between these modes and the surface mass balance of the Patagonian Icefields.