Monthly Temperature Fields Research Articles

MESMER-M: an Earth system model emulator for spatially resolved monthly temperature

Abstract. The degree of trust placed in climate model projections is commensurate with how well their uncertainty can be quantified, particularly at timescales relevant to climate policy makers. On inter-annual to decadal timescales, model projection uncertainty due to natural variability dominates at the local level and is imperative to describing near-term and seasonal climate events but difficult to quantify owing to the computational constraints of producing large ensembles. To this extent, emulators are valuable tools for approximating climate model runs, allowing for the exploration of the uncertainty space surrounding selected climate variables at a substantially reduced computational cost. Most emulators, however, operate at annual to seasonal timescales, leaving out monthly information that may be essential to assessing climate impacts. This study extends the framework of an existing spatially resolved, annual-scale Earth system model (ESM) emulator (MESMER, Beusch et al., 2020) by a monthly downscaling module (MESMER-M), thus providing local monthly temperatures from local yearly temperatures. We first linearly represent the mean response of the monthly temperature cycle to yearly temperatures using a simple harmonic model, thus maintaining month-to-month correlations and capturing changes in intra-annual variability. We then construct a month-specific local variability module which generates spatio-temporally correlated residuals with yearly temperature- and month-dependent skewness incorporated within. The emulator's ability to capture the yearly temperature-induced monthly temperature response and its surrounding uncertainty due to natural variability is demonstrated for 38 different ESMs from the sixth phase of the Coupled Model Intercomparison Project (CMIP6). The emulator is furthermore benchmarked using a simple gradient-boosting-regressor-based model trained on biophysical information. We find that while regional-scale, biophysical feedbacks may induce non-uniformities in the yearly to monthly temperature downscaling relationship, statistical emulation of regional effects shows comparable skill to the more physically informed approach. Thus, MESMER-M is able to statistically generate ESM-like, large initial-condition ensembles of spatially explicit monthly temperature fields, providing monthly temperature probability distributions which are of critical value to impact assessments.

Bias correction of monthly precipitation and temperature fields from Intergovernmental Panel on Climate Change AR4 models using equidistant quantile matching

A new quantile‐based mapping method is developed for the bias correction of monthly global circulation model outputs. Compared to the widely used quantile‐based matching method that assumes stationarity and only uses the cumulative distribution functions (CDFs) of the model and observations for the baseline period, the proposed method incorporates and adjusts the model CDF for the projection period on the basis of the difference between the model and observation CDFs for the training (baseline) period. Thus, the method explicitly accounts for distribution changes for a given model between the projection and baseline periods. We demonstrate the use of the new method over northern Eurasia. We fit a four‐parameter beta distribution to monthly temperature fields and discuss the sensitivity of the results to the choice of distribution range parameters. For monthly precipitation data, a mixed gamma distribution is used that accounts for the intermittent nature of rainfall. To test the fidelity of the proposed method, we choose 1970–1999 as the baseline training period and then randomly select 30 years from 1901–1999 as the projection test period. The bootstrapping is repeated 30 times to mimic different climate conditions that may occur, and the results suggest that both methods are comparable when applied to the 20th century for both temperature and precipitation for the examined quartiles. We also discuss the dependence of the bias correction results on the choice of time period for training. This indicates that the remaining biases in the bias‐corrected time series are directly tied to the model's performance during the training period, and therefore care should be taken when using a particular training time period. When applied to the Intergovernmental Panel on Climate Change fourth assessment report (AR4) A2 climate scenario projection, the data time series after bias correction from both methods exhibit similar spatial patterns. However, over regions where the climate model shows large changes in projected variability, there are discernable differences between the methods. The proposed method is more sensitive to a reduction in variability, exemplified by wintertime temperature. Further synthetic experiments using the lower 33% and upper 33% of the full data set as the validation data suggest that the proposed equidistance quantile‐matching method is more efficient in reducing biases than the traditional CDF mapping method for changing climates, especially for the tails of the distribution. This has important consequences for the occurrence and intensity of future projected extreme events such as heat waves, floods, and droughts. As the new method is simple to implement and does not require substantial computational time, it can be used to produce auxiliary ensemble scenarios for various climate impact‐oriented applications.

Rate of Change of Heat Storage of the World Ocean

Abstract Results or a Fourier analysis of climatological fields of the monthly rate of change of heat storage for the world ocean are presented. The amplitude and Phase of the first harmonic are shown, as well as the percent variance of the annual cycle accounted for by this harmonic. These distributions are presented to describe the global geographical characteristics of the annual cycle of the rate of change of heat storage of the world ocean integrated through a depth of 275 m. We have used the results of our Fourier analysis at each gridpoint to synthesize monthly mean estimates of the rate of change of heat storage based on the annual mean and first two harmonies of the annual cycle. We have zonally averaged these time-smoothed monthly estimates over the world ocean and individual ocean basins and present time-latitude plots of these zonal averages. The climatological monthly temperature fields used to estimate heat storage are one-degree objectively analyzed fields which are based on the approximate...