The influence of atmospheric circulation on the mid-Holocene climate of Europe: a data-model comparison

The atmospheric circulation is a key area of uncertainty in climate model simulations of future climate change, especially in mid-latitude regions such as Europe where atmospheric dynamics have a significant role in climate variability. It has been proposed that the mid-Holocene was characterized in Europe by a stronger westerly circulation in winter comparable with a more positive AO/NAO, and a weaker westerly circulation in summer caused by anticyclonic blocking near Scandinavia. Model simulations indicate at best only a weakly positive AO/NAO, whilst changes in summer atmospheric circulation have not been widely investigated. Here we use a new pollen-based reconstruction of European mid-Holocene climate to investigate the role of atmospheric circulation in explaining the spatial pattern of seasonal temperature and precipitation anomalies. We find that the footprint of the anomalies is entirely consistent with those from modern analogue atmospheric circulation patterns associated with a strong westerly circulation in winter (positive AO/NAO) and a weak westerly circulation in summer associated with anti-cyclonic blocking (positive SCAND). We find little agreement between the reconstructed anomalies and those from 14 GCMs that performed mid-Holocene experiments as part of the PMIP3/CMIP5 project, which show a much greater sensitivity to top-of-the-atmosphere changes in solar insolation. Our findings are consistent with data-model comparisons on contemporary timescales that indicate that models underestimate the role of atmospheric circulation in recent climate change, whilst also highlighting the importance of atmospheric dynamics in explaining interglacial warming.

The climate of Europe during the Holocene: a gridded pollen-based reconstruction and its multi-proxy evaluation

Pamela Collins, Basil Andrew Stansfield Davis, Jed Oliver Kaplan

We present a new gridded climate reconstruction for Europe for the last 12,000 years based on pollen data. The reconstruction is an update of Davis et al. (2003) using the same methodology, but with a greatly expanded fossil and surface-sample dataset and more rigorous quality-control. The modern pollen dataset has been increased by more than 80%, and the fossil pollen dataset by more than 50%, representing almost 60,000 individual pollen samples. The climate parameters reconstructed include summer/winter and annual temperatures and precipitation, as well as a measure of moisture balance, and growing degree-days above 5 degrees C. Confidence limits were established for the reconstruction based on transfer function and interpolation uncertainties. The reconstruction takes account of post-glacial isostatic readjustment which resulted in a potential warming bias of up to +1-2 degrees C for parts of Fennoscandia in the early Holocene, as well as changes in palaeogeography resulting from decaying ice sheets and rising post-glacial sea-levels. This new dataset has been evaluated against previously published independent quantitative climate reconstructions from a variety of archives on a site-by-site basis across Europe. The results of this comparison are generally very good; only chironomid-based reconstructions showed substantial differences with our values. Our reconstruction is available for download as gridded maps throughout the Holocene on a 1000-year time-step. The gridded format makes our reconstructions suitable for comparison with climate model output and for other applications such as vegetation and land-use modelling. Our new climate reconstruction suggests that warming in Europe during the mid-Holocene was greater in winter than in summer, an apparent paradox that is not consistent with current climate model simulations and traditional interpretations of Milankovitch theory. (C) 2015 Elsevier Ltd. All rights reserved.

Pergamon-Elsevier Science Ltd2015

Geophysical Applications of Vegetation Modeling

Jed Oliver Kaplan

This thesis describes the development and selected applications of a global vegetation model, BIOME4. The model is applied to problems in high-latitude vegetation distribution and climate, trace gas production, and isotope biogeochemistry. It demonstrates how a modeling approach, based on principles of plant physiology and ecology, can be applied to interdisciplinary problems that cannot be adequately addressed by direct observations or experiments. The work is relevant to understanding the potential effects of climate change on the terrestrial biosphere and the feedbacks between the biosphere and climate. BIOME4 simulates the distribution of 15 high-latitude biomes, including five tundra vegetation types, for the present day using observed climate, and the LGM, mid-Holocene, and a “greenhouse” scenario for 2100 using the output of GCMs. In the LGM simulations, the high-latitudes show a marked increase in the area of graminoid and forb tundra, which is also the predominant feature in the paleodata. This vegetation has no widespread modern analog; it was favored by the cold, dry climate, and supported large mammoth populations. Mid-Holocene simulations indicate a modest, asymmetrical northward advance of the Arctic treeline compared to present, with greatest extension in central Siberia (up to 300 km), and little to no change in the Western Hemisphere. This result is in good agreement with pollen and megafossil data from the same period. Differential warming of the continents in response to increased high-latitude solar radiation is hypothesized to account for the asymmetry. Vegetation changes in the 2100 projection, which assumes a continued exponential increase in atmospheric GHG concentrations, are more radical than those simulated for the mid-Holocene. The year-round forcing due to GHGs increases both summertime and annual temperatures in the high latitudes by up to double the mid-Holocene anomaly. However the potential treeline advances and biome shifts in our simulation are unlikely to be realized within 100 years, because of the time required for migration and establishment of new vegetation types. Potential natural wetland area for the present day was simulated by BIOME4 as 11.0x106km2. This value is higher than other estimates but includes small (

Lund University2001

Development of probability distributions for regional climate change from uncertain global mean warming and an uncertain scaling relationship

Abdelkader Mezghani

A method is presented to produce probability distributions for regional climate change in surface temperature and precipitation. The method combines a probability distribution for global mean temperature increase with the probability distributions for the appropriate scaling variables, i.e., the changes in regional temperature/precipitation per degree global mean warming. The distribution of each scaling variable is assumed to be normal. The uncertainty of the scaling relationship arises from systematic differences between the regional changes from global and regional climate model simulations and from natural variability. The contributions of these sources of uncertainty to the total variance of the scaling variable are estimated from simulated temperature and precipitation data in a suite of regional climate model experiments conducted in the framework of the EU funded project PRUDENCE, using an Analysis Of Variance (ANOVA). Five Case Study Regions (CSRs) are considered: NW England, the Rhine basin, Iberia, Jura lakes (Switzerland) and Mauvoisin dam (Switzerland). These CSRs cover the area considered in the EU funded project SWURVE. The resulting regional climate changes for 2070-2099 vary quite significantly between CSRs, between seasons, and between meteorological variables. A notable point is that for all CSRs the expected warming in summer is higher than the expected warming for the other seasons. This summer warming is accompanied by a large decrease in precipitation. The uncertainty of the scaling ratios for temperature and precipitation is relatively large in summer due to the differences between regional climate models. Differences between the spatial climate-change patterns of global climate model simulations significantly contribute to the uncertainty of the scaling ratio for temperature. However, for the scaling ratio for precipitation sometimes no meaningful contribution could be found due to the small number of global climate models in the PRUDENCE project and the large influence of natural variability. For precipitation, natural variability is often the largest source of uncertainty. By contrast, for temperature the contribution of natural variability to the total variance of the scaling ratio is small, in particular for the annual mean values. Simulation from the probability distributions of global mean warming and the scaling ratio results in a wider range of regional temperature change than that in the regional climate model experiments. For the regional change of precipitation, however, a large proportion of the simulations (about 90%) is within the range of the regional climate model simulations.

2007