On the Correspondence between Mean Forecast Errors and Climate Errors in CMIP5 Models

Abstract

The present study examines the correspondence between short-and long-term systematic errors in five atmospheric models by comparing the 16 five-day hindcast ensembles from the Transpose Atmospheric Model Intercomparison Project II (Transpose-AMIP II) for July-August 2009 (short term) to the climate simulations from phase 5 of the Coupled Model Intercomparison Project (CMIP5) and AMIP for the JuneAugust mean conditions of the years of 1979-2008 (long term). Because the short-term hindcasts were conducted with identical climate models used in the CMIP5/AMIP simulations, one can diagnose over what time scale systematic errors in these climate simulations develop, thus yielding insights into their origin through a seamless modeling approach.The analysis suggests that most systematic errors of precipitation, clouds, and radiation processes in the long-term climate runs are present by day 5 in ensemble average hindcasts in all models. Errors typically saturate after few days of hindcasts with amplitudes comparable to the climate errors, and the impacts of initial conditions on the simulated ensemble mean errors are relatively small. This robust bias correspondence suggests that these systematic errors across different models likely are initiated by model parameterizations since the atmospheric large-scale states remain close to observations in the first 2-3 days. However, biases associated with model physics can have impacts on the large-scale states by day 5, such as zonal winds, 2-m temperature, and sea level pressure, and the analysis further indicates a good correspondence between shortand long-term biases for these large-scale states. Therefore, improving individual model parameterizations in the hindcast mode could lead to the improvement of most climate models in simulating their climate mean state and potentially their future projections.

Description and basic evaluation of Beijing Normal University Earth System Model (BNU-ESM) version 1

“…8b and 5b). Such as the negative temperature bias over South Africa is likely linked to the negative SWCF bias and excessive cloud fraction, and the positive temperature bias over central USA is probably linked to less cloud fraction (Ma et al, 2014). The global average precipitation in BNU-ESM is 0.18 mm day −1 larger over the period of 1979-2005 year (Fig.…”

The Decadal Climate Prediction Project (DCPP) contribution to CMIP6

“…For instance, Ma et al (2014) analyze the timescale over which systematic errors develop, thus yielding insights into their origin. Also, as forecasts evolve, they lose initial condition information and approach a forced climate state giving information also on this behaviour.…”

Simulating North American mesoscale convective systems with a convection-permitting climate model

“…In this situation the correct representation of local-scale processes such as soil-atmosphere interactions, regional-scale wind systems, and mixing in the planetary boundary layer are essential. A summertime warm and dry bias over the central US is fairly common in weather and climate models (e.g., Klein et al 2006;Ma et al 2014;Bellprat et al 2016). We are currently performing sensitivity experiments to find the sources and potential solutions for these biases.…”