Abstract

AbstractEarth System Models (ESMs) project climate change, but they often contain biases in their estimates of contemporary climate that propagate into simulated futures. Land models translate climate projections into surface impacts, but these will be inaccurate if ESMs have substantial errors. Bias concerns are relevant for terrestrial physiological processes which often respond non‐linearly (i.e. contain threshold responses) and are therefore sensitive to absolute environmental conditions as well as changes. We bias‐correct the UK Met Office ESM, HadGEM2‐ES, against the CRU–JRA observation‐based gridded estimates of recent climate. We apply the derived bias corrections to future projections by HadGEM2‐ES for the RCP8.5 scenario of future greenhouse gas concentrations. Focusing on South America, the bias correction includes adjusting for ESM estimates that, annually, are approximately 1 degree too cold, for comparison against 21st Century warming of around 4 degrees. Locally, these values can be much higher. The ESM is also too wet on average, by approximately 1 mm·day−1, which is substantially larger than the mean predicted change. The corrected climate fields force the Joint UK Land Environment Simulator (JULES) dynamic global vegetation model to estimate land surface changes, with an emphasis on the carbon cycle. Results show land carbon sink reductions across South America, and in some locations, the net land–atmosphere CO2 flux becomes a source to the atmosphere by the end of this century. Transitions to a CO2 source is where increases in plant net primary productivity are offset by larger enhancements in soil respiration. Bias‐corrected simulations estimate the rise in South American land carbon stocks between pre‐industrial times and the end of the 2080s is ∼12 GtC lower than that without climate bias removal, demonstrating the importance of merging historical observational meteorological forcing with ESM diagnostics. We present evidence for a substantial climate‐induced role of greater soil decomposition in the fate of the Amazon carbon sink.

Highlights

  • Climate change is a defining issue of our time

  • We present the effect of mean bias correction for temperature, for four representative months, and at a single location of the gridbox containing the city of Manaus (Figure 1)

  • We base this addition on past variations described by the Climate Research Unit (CRU)–Japanese Re-Analysis product (JRA) data, covering timescales from interannual variation down to 6 hourly (Section 2.2)

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Summary

Introduction

Climate change is a defining issue of our time. To understand and derive robust mitigation strategies to climate change requires accurate estimates of near-surface climate and its change under differing greenhouse gas (GHG) emissions scenarios. The end of the spin-up generates the initial conditions for the transient calculations that determine climate responses as GHG levels change from their pre-industrial values This numerical structure means that ESMs estimate the period between pre-industrial times and present, in addition to the decades ahead. Under the assumption that these errors are likely to propagate into simulations of future periods, we can remove them to refine estimates of any climate change ahead Such bias correction ensures a smooth transition between the present-day end to historical observation-based data sets and model-based projections of future change. Corrections allow the combined use of historical data, followed by more accurate ESM-based assessment of future climate, and with no jumps in time series for the present day These combined estimates of evolving near-surface climate are available to drive terrestrial impacts models. As many ecosystem processes depend on the absolute value of near-surface climate conditions, bias-corrected drivers will likely enhance how well they are modelled

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