Regional climate change patterns identified by cluster analysis

Climate change caused by anthropogenic greenhouse emissions leads to impacts on a global and a regional scale. A quantitative picture of the projected changes on a regional scale can help to decide on appropriate mitigation and adaptation measures. In the past, regional climate change results have often been presented on rectangular areas. But climate is not bound to a rectangular shape and each climate variable shows a distinct pattern of change. Therefore, the regions over which the simulated climate change results are aggregated should be based on the variable(s) of interest, on current mean climate as well as on the projected future changes. A cluster analysis algorithm is used here to define regions encompassing a similar mean climate and similar projected changes. The number and the size of the regions depend on the variable(s) of interest, the local climate pattern and on the uncertainty introduced by model disagreement. The new regions defined by the cluster analysis algorithm include information about regional climatic features which can be of a rather small scale. Comparing the regions used so far for large scale regional climate change studies and the new regions it can be shown that the spacial uncertainty of the projected changes of different climate variables is reduced significantly, i.e. both the mean climate and the expected changes are more consistent within one region and therefore more representative for local impact

Future climate resources for tourism in Europe based on the daily Tourism Climatic Index

Climate is an important resource for many types of tourism. One of several metrics for the suitability of climate for sightseeing is Mieczkowski's "Tourism Climatic Index” (TCI), which summarizes and combines seven climate variables. By means of the TCI, we analyse the present climate resources for tourism in Europe and projected changes under future climate change. We use daily data from five regional climate models and compare the reference period 1961-1990 to the A2 scenario in 2071-2100. A comparison of the TCI based on reanalysis data and model simulations for the reference period shows that current regional climate models capture the important climatic patterns. Currently, climate resources are best in Southern Europe and deteriorate with increasing latitude and altitude. With climate change the latitudinal band of favourable climate is projected to shift northward improving climate resources in Northern and Central Europe in most seasons. Southern Europe's suitability for sightseeing tourism drops strikingly in the summer holiday months but is partially compensated by considerable improvements between October and Apri

Local eigenvalue analysis of CMIP3 climate model errors

Of the two dozen or so global atmosphere—ocean general circulation models (AOGCMs), many share parameterizations, components or numerical schemes, and several are developed by the same institutions. Thus it is natural to suspect that some of the AOGCMs have correlated error patterns. Here we present a local eigenvalue analysis for the AOGCM errors based on statistically quantified correlation matrices for these errors. Our statistical method enables us to assess the significance of the result based on the simulated data under the assumption that all AOGCMs are independent. The result reveals interesting local features of the dependence structure of AOGCM errors. At least for the variable and the timescale considered here, the Coupled Model Intercomparison Project phase 3 (CMIP3) model archive cannot be treated as a collection of independent models.We use multidimensional scaling to visualize the similarity of AOGCMs and all-subsets regression to provide subsets of AOGCMs that are the best approximation to the variation among the full set of models.ISSN:0280-6495ISSN:1600-087

Modeled seasonality of glacial abrupt climate events

Greenland ice cores, as well as many other paleo-archives from the northern hemisphere, recorded a series of 25 warm interstadial events, the so-called Dansgaard-Oeschger (D-O) events, during the last glacial period. We use the three-dimensional coupled global ocean-atmosphere-sea ice model ECBILT-CLIO and force it with freshwater input into the North Atlantic to simulate abrupt glacial climate events, which we use as analogues for D-O events. We focus our analysis on the Northern Hemisphere. The simulated events show large differences in the regional and seasonal distribution of the temperature and precipitation changes. While the temperature changes in high northern latitudes and in the North Atlantic region are dominated by winter changes, the largest temperature increases in most other land regions are seen in spring. Smallest changes over land are found during the summer months. Our model simulations also demonstrate that the temperature and precipitation change patterns for different intensifications of the Atlantic meridional overturning circulation are not linear. The extent of the transitions varies, and local non-linearities influence the amplitude of the annual mean response as well as the response in different seasons. Implications for the interpretation of paleo-records are discusse

Improved simulation of extreme precipitation in a high-resolution atmosphere model

Climate models often underestimate the magnitude of extreme precipitation. We compare the performance of a high-resolution (∼0.25°) time-slice atmospheric simulation (1979–2005) of the Community Earth System Model 1.0 in representing daily extreme precipitation events against those of the same model at lower resolutions (∼1° and 2°). We find significant increases in the simulated levels of daily extreme precipitation over Europe, the United States, and Australia. In many cases the increase in high percentiles (>95th) of daily precipitation leads to better agreement with observational data sets. For lower percentiles, we find that increasing resolution does not significantly increase values of simulated precipitation. We argue that the reduced biases mainly result from the higher resolution models resolving more key physical processes controlling heavy precipitation. We conclude that while high resolution is vital for accurately simulating extreme precipitation, considerable biases remain at the highest available model resolutions

Pacific variability reconciles observed and modelled global mean temperature increase since 1950

Global mean temperature change simulated by climate models deviates from the observed temperature increase during decadal-scale periods in the past. In particular, warming during the ‘global warming hiatus’ in the early twenty-first century appears overestimated in CMIP5 and CMIP6 multi-model means. We examine the role of equatorial Pacific variability in these divergences since 1950 by comparing 18 studies that quantify the Pacific contribution to the ‘hiatus’ and earlier periods and by investigating the reasons for differing results. During the ‘global warming hiatus’ from 1992 to 2012, the estimated contributions differ by a factor of five, with multiple linear regression approaches generally indicating a smaller contribution of Pacific variability to global temperature than climate model experiments where the simulated tropical Pacific sea surface temperature (SST) or wind stress anomalies are nudged towards observations. These so-called pacemaker experiments suggest that the ‘hiatus’ is fully explained and possibly over-explained by Pacific variability. Most of the spread across the studies can be attributed to two factors: neglecting the forced signal in tropical Pacific SST, which is often the case in multiple regression studies but not in pacemaker experiments, underestimates the Pacific contribution to global temperature change by a factor of two during the ‘hiatus’; the sensitivity with which the global temperature responds to Pacific variability varies by a factor of two between models on a decadal time scale, questioning the robustness of single model pacemaker experiments. Once we have accounted for these factors, the CMIP5 mean warming adjusted for Pacific variability reproduces the observed annual global mean temperature closely, with a correlation coefficient of 0.985 from 1950 to 2018. The CMIP6 ensemble performs less favourably but improves if the models with the highest transient climate response are omitted from the ensemble mean

Impact of short-lived non-CO2 mitigation on carbon budgets for stabilizing global warming

Limiting global warming to any level requires limiting the total amount of CO2 emissions, or staying within a CO2 budget. Here we assess how emissions from short-lived non-CO2 species like methane, hydrofluorocarbons (HFCs), black-carbon, and sulphates influence these CO2 budgets. Our default case, which assumes mitigation in all sectors and of all gases, results in a CO2 budget between 2011–2100 of 340 PgC for a >66% chance of staying below 2°C, consistent with the assessment of the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Extreme variations of air-pollutant emissions from black-carbon and sulphates influence this budget by about ±5%. In the hypothetical case of no methane or HFCs mitigation—which is unlikely when CO2 is stringently reduced—the budgets would be much smaller (40% or up to 60%, respectively). However, assuming very stringent CH4 mitigation as a sensitivity case, CO2 budgets could be 25% higher. A limit on cumulative CO2 emissions remains critical for temperature targets. Even a 25% higher CO2 budget still means peaking global emissions in the next two decades, and achieving net zero CO2 emissions during the third quarter of the 21st century. The leverage we have to affect the CO2 budget by targeting non-CO2 diminishes strongly along with CO2 mitigation, because these are partly linked through economic and technological factors

Robust detection and attribution of climate change under interventions

Fingerprints are key tools in climate change detection and attribution (D&A) that are used to determine whether changes in observations are different from internal climate variability (detection), and whether observed changes can be assigned to specific external drivers (attribution). We propose a direct D&A approach based on supervised learning to extract fingerprints that lead to robust predictions under relevant interventions on exogenous variables, i.e., climate drivers other than the target. We employ anchor regression, a distributionally-robust statistical learning method inspired by causal inference that extrapolates well to perturbed data under the interventions considered. The residuals from the prediction achieve either uncorrelatedness or mean independence with the exogenous variables, thus guaranteeing robustness. We define D&A as a unified hypothesis testing framework that relies on the same statistical model but uses different targets and test statistics. In the experiments, we first show that the CO2 forcing can be robustly predicted from temperature spatial patterns under strong interventions on the solar forcing. Second, we illustrate attribution to the greenhouse gases and aerosols while protecting against interventions on the aerosols and CO2 forcing, respectively. Our study shows that incorporating robustness constraints against relevant interventions may significantly benefit detection and attribution of climate change