A Bayesian partition modelling approach to resolve spatial variability in climate records from borehole temperature inversion

International audienceCollections of suitably chosen borehole profiles can be used to infer large-scale trends in ground-surface temperature (GST) histories for the past few hundred years. These reconstructions are based on a large database of carefully selected borehole temperature measurements from around the globe. Since non-climatic thermal influences are difficult to identify, representative temperature histories are derived by averaging individual reconstructions to minimize the influence of these perturbing factors. This may lead to three potentially important drawbacks: the net signal of non-climatic factors may not be zero, meaning that the average does not reflect the best estimate of past climate; the averaging over large areas restricts the useful amount of more local climate change information available; and the inversion methods used to reconstruct the past temperatures at each site must be mathematically identical and are therefore not necessarily best suited to all data sets. In this work, we avoid these issues by using a Bayesian partition model (BPM), which is computed using a trans-dimensional form of aMarkov chainMonte Carlo algorithm. This then allows the number and spatial distribution of different GST histories to be inferred from a given set of borehole data by partitioning the geographical area into discrete partitions. Profiles that are heavily influenced by non-climatic factors will be partitioned separately. Conversely, profiles with climatic information, which is consistent with neighbouring profiles, will then be inferred to lie in the same partition. The geographical extent of these partitions then leads to information on the regional extent of the climatic signal. In this study, three case studies are described using synthetic and real data. The first demonstrates that the Bayesian partition model method is able to correctly partition a suite of synthetic profiles according to the inferred GST history. In the second, more realistic case, a series of temperature profiles are calculated using surface air temperatures of a global climate model simulation. In the final case, 23 real boreholes from the United Kingdom, previously used for climatic reconstructions, are examined and the results compared with a local instrumental temperature series and the previous estimate derived from the same borehole data. The results indicate that the majority (17) of the 23 boreholes are unsuitable for climatic reconstruction purposes, at least without including other thermal processes in the forward model

Reassessing the Value of Regional Climate Modeling Using Paleoclimate Simulations

Regional climate models (RCMs) are often assumed to be more skillful compared to lower-resolution general circulation models (GCM). However, RCMs are driven by input from coarser resolution GCMs, which may introduce biases. This study employs versions of the HadAMB3 GCM at three resolutions (>50 km) to investigate the added value of higher resolution using identically configured simulations of the preindustrial (PI), mid-Holocene, and Last Glacial Maximum. The RCM shows improved PI climatology compared to the coarse-resolution GCM and enhanced paleoanomalies in the jet stream and storm tracks. However, there is no apparent improvement when compared to proxy reconstructions. In the high-resolution GCM, accuracy in PI climate and atmospheric anomalies are enhanced despite its intermediate resolution. This indicates that synoptic and mesoscale features in a RCM are influenced by its low-resolution input, which impacts the simulated climatology. This challenges the paradigm that RCMs improve the representation of climate conditions and change.Peer reviewe

A simulated Northern Hemisphere terrestrial climate dataset for the past 60,000 years (vol 6, 265, 2019)

Peer reviewe

A simulated Northern Hemisphere terrestrial climate dataset for the past 60,000 years

We present a continuous land-based climate reconstruction dataset extending back 60 kyr from 0 BP (1950) at 0.5 degrees resolution on a monthly timestep for 0 degrees N to 90 degrees N. It has been generated from 42 discrete snapshot simulations using the HadCM3B-M2.1 coupled general circulation model. We incorporate Dansgaard-Oeschger (DO) and Heinrich events to represent millennial scale variability, based on a temperature reconstruction from Greenland ice-cores, with a spatial fingerprint based on a freshwater hosing simulation with HadCM3B-M2.1. Interannual variability is also added and derived from the initial snapshot simulations. Model output has been downscaled to 0.5 degrees resolution (using simple bilinear interpolation) and bias corrected. Here we present surface air temperature, precipitation, incoming shortwave energy, minimum monthly temperature, snow depth, wind chill and number of rainy days per month. This is one of the first open access climate datasets of this kind and can be used to study the impact of millennial to orbital-scale climate change on terrestrial greenhouse gas cycling, northern extra-tropical vegetation, and megaflora and megafauna population dynamics.Peer reviewe

Limited response of peatland CH4 emissions to abrupt Atlantic Ocean circulation changes in glacial climates

Ice-core records show that abrupt Dansgaard–Oeschger (D–O) climatic warming events of the last glacial period were accompanied by large increases in the atmospheric CH4 concentration (up to 200 ppbv). These abrupt changes are generally regarded as arising from the effects of changes in the Atlantic Ocean meridional overturning circulation and the resultant climatic impact on natural CH4 sources, in particular wetlands. We use two different ecosystem models of wetland CH4 emissions to simulate northern CH4 sources forced with coupled general circulation model simulations of five different time periods during the last glacial to investigate the potential influence of abrupt ocean circulation changes on atmospheric CH4 levels during D–O events. The simulated warming over Greenland of 7–9 °C in the different time periods is at the lower end of the range of 11–15 °C derived from ice cores, but is associated with strong impacts on the hydrological cycle, especially over the North Atlantic and Europe during winter. We find that although the sensitivity of CH4 emissions to the imposed climate varies significantly between the two ecosystem emissions models, the model simulations do not reproduce sufficient emission changes to satisfy ice-core observations of CH4 increases during abrupt events. The inclusion of permafrost physics and peatland carbon cycling in one model (LPJ-WHyMe) increases the climatic sensitivity of CH44 emissions relative to the Sheffield Dynamic Global Vegetation Model (SDGVM) model, which does not incorporate these processes. For equilibrium conditions this additional sensitivity is mostly due to differences in carbon cycle processes, whilst the increased sensitivity to the imposed abrupt warmings is also partly due to the effects of freezing on soil thermodynamics. These results suggest that alternative scenarios of climatic change could be required to explain the abrupt glacial CH4 variations, perhaps with a more dominant role for tropical wetland CH4 sources

Impact of abrupt sea ice loss on Greenland water isotopes during the last glacial period

Greenland ice cores provide excellent evidence of past abrupt climate changes. However, there is no universally accepted theory of how and why these Dansgaard–Oeschger (DO) events occur. Several mechanisms have been proposed to explain DO events, including sea ice, ice shelf buildup, ice sheets, atmospheric circulation, and meltwater changes. DO event temperature reconstructions depend on the stable water isotope (δ18O) and nitrogen isotope measurements from Greenland ice cores: interpretation of these measurements holds the key to understanding the nature of DO events. Here, we demonstrate the primary importance of sea ice as a control on Greenland ice core δ18O: 95% of the variability in δ18O in southern Greenland is explained by DO event sea ice changes. Our suite of DO events, simulated using a general circulation model, accurately captures the amplitude of δ18O enrichment during the abrupt DO event onsets. Simulated geographical variability is broadly consistent with available ice core evidence. We find an hitherto unknown sensitivity of the δ18O paleothermometer to the magnitude of DO event temperature increase: the change in δ18O per Kelvin temperature increase reduces with DO event amplitude. We show that this effect is controlled by precipitation seasonality