Simulated Temperature Variability Research Articles

FROT: A Framework to comprehensively describe radiative contributions to temperature responses

Abstract Different human activities and associated emissions of CO2 and non-CO2 radiative forcing agents and feedbacks determine the final state of Earth’s climate. To understand and explain contributions to global temperature changes, many emission-based metrics have been employed, such as CO2-equivalent or -forcing equivalent. None of these metrics, however, include dynamic responses from Earth system feedbacks in terms of carbon and heat redistribution, known to play an increasingly important role in ambitious mitigation scenarios. Here we introduce a framework that allows for an assessment of such feedbacks in addition to CO2, non-CO2 anthropogenic forcing and natural external variability contributions. FROT (Framework for Radiative cOntributions to Temperature response) allows for an assessment of components of direct radiative impact to the system (climate forcing), as well as Earth system feedbacks concerning heat and carbon. The framework is versatile in terms of applications and allows for exploring individual components contributions to, for example, temperature stabilisation simulations, or comparisons in different models and scenarios, as it can reasonably explain their simulated temperature variability. Here, we apply FROT to both an intermediate complexity and a fully coupled Earth system model, as we simulate highly ambitious mitigation scenarios. Comparing temperature stabilisation scenarios, we can show that both net-zero CO2 emissions and small amounts of positive CO2 emissions could lead to a stable global temperature trajectory. Our assessment reveals that the effects of non-CO2 climate forcings, especially the development of sulphate aerosols in the atmosphere, and the dynamics of the carbon cycle, play a pivotal role in the final level of warming and in enabling a temperature stabilisation. Under highly ambitious climate mitigation scenarios it becomes crucial to include Earth system feedbacks, specifically ocean heat uptake, to understand interannual to decadal temperature development, since previously secondary processes now become increasingly dominant. Our framework offers the opportunity to do so.

Understanding the cold biases of CMIP5 models over China with weather regimes

Most models in the fifth phase of the Coupled Model Intercomparison Project (CMIP5) underestimate the surface air temperature over China in both winter and summer. Understanding the weather regime in association with the simulated temperature variability is of high interest to get insight into those biases. Based on the weather regime method, we investigated the contributions of large-scale dynamics and non-dynamical processes to temperature biases and inter-model spread. The weather regimes associated with the observational temperature patterns were obtained through a k-means clustering algorithm applied to daily 500 hPa geopotential height anomalies. Here we identified the clustering number of weather regimes using the classifiability and reproducibility indices which can provide the optimal clustering number to obtain objective clustering. Both indices suggested the weather regimes in East Asia can be classified as four clusters in winter (December–January–February) and three in summer (June–July–August). The results indicated that the first and second weather regimes were related to the cold temperature anomalies in China during winter, and the three weather regimes could not effectively classify the temperature patterns during summer. The ensemble mean of 23 CMIP5 models overestimated the occurrence frequencies of the second weather regime, which corresponds to a weaker high latitude westerly jet over East Asia during winter. The 500 hPa geopotential height anomalies and the inter-model spread over the Tibetan Plateau may be associated with the limited ability of the CMIP5 models in simulating the thermal effects of plateau in summer. We also found that the non-dynamical processes had major contribution to the ensemble-mean biases, and the large-scale dynamics played a minor role. The non-dynamical processes substantially affected the inter-model spread, especially over the Tibetan Plateau and the Sichuan Basin, during both winter and summer. The results suggested that improving the simulation of regional processes may help to improve model performance. The use of multi-model mean is recommended since it performs better than most of individual models.

An Assessment of Observed and Simulated Temperature Variability in Sierra de Guadarrama

This work provides a first assessment of temperature variability at interannual and decadal timescales in Sierra de Guadarrama, a high mountain protected area of the Central System in the Iberian Peninsula. Observational data from stations located in the area and simulated data from a high-resolution simulation (1 km) with the Weather Research and Forecasting (WRF) model, fed from ERA Interim reanalysis, are used in order to analyse the temperature variability in the period 2000–2018. Comparison among all datasets allows evaluation of the realism of the model simulations. The results show that the model tends to underestimate the observational mean temperatures and anomalies at high-altitude stations. A linear mean temperature vertical gradient of −5.81 °C/km is observed, but it is overestimated by the model (−6.56 °C/km). The variability of the daily temperature anomalies for both observations and, to a lesser extent, simulations increases with height. The added value that the WRF offers against the use of the ERA Interim is evaluated. The results show that the WRF provides a better performance than the reanalysis, as it shows smaller biases with respect to observational temperature anomalies. Finally, the study of temperature trends over the Sierra de Guadarrama and its surroundings for the period 2000–2018 shows a warming in the area, significantly pronounced in autumn. When extended to the last decades, observations show that this warming has been happening since the first half of the 20th century, especially during the period 1970–2018, but not as much as during 2000–2018.

Modeling land‐climate coupling in Europe: Impact of land surface representation on climate variability and extremes

Land‐climate coupling has been shown to be important for European summer climate variability and extreme events. However, the sensitivity of these feedbacks to land surface model (LSM) choice has been little investigated up to now. In this study, we assess the impact of the LSM on the simulated climate variability in a regional climate model (RCM). The experiments were conducted with the COSMO‐CLM2RCM. COSMO‐CLM2can be run with two alternative LSMs, the 2nd‐generation LSM TERRA_ML or the more sophisticated 3rd‐generation LSM Community Land Model (CLM3.5). The analyzed simulations include control and sensitivity experiments with prescribed soil moisture (dry or wet). Using CLM3.5 instead of TERRA_ML improves the simulated temperature variability by alleviating an overestimation of temperature inter‐annual variability in the RCM. Also, the representation of the probability density functions of daily maximum summer temperature is improved when using the more advanced LSM. The reduced climate variability is linked to a larger ground heat flux and smaller variability in soil moisture and short‐wave radiation. The latter effect results from the coupling of the LSM to the atmospheric module. In addition, using CLM3.5 reduces the sensitivity of COSMO‐CLM2to extreme soil moisture conditions. An analysis assessing the relationship between the standard precipitation index and the subsequent number of hot days in summer reveals a better representation of this relationship using CLM3.5. Hence, we find that biases in climate variability and extremes can be reduced and the representation of land‐climate coupling can be improved with the use of the more sophisticated LSM.

Effect of flux adjustments on temperature variability in climate models

It has been suggested that “flux adjustments” in climate models suppress simulated temperature variability. If true, this might invalidate the conclusion that at least some of observed temperature increases since 1860 are anthropogenic, since this conclusion is based in part on estimates of natural temperature variability derived from flux‐adjusted models. We assess variability of surface air temperatures in 17 simulations of internal temperature variability submitted to the Coupled Model Intercomparison Project. By comparing variability in flux‐adjusted vs. non‐flux adjusted simulations, we find no evidence that flux adjustments suppress temperature variability in climate models; other, largely unknown, factors are much more important in determining simulated temperature variability. Therefore the conclusion that at least some of observed temperature increases are anthropogenic cannot be questioned on the grounds that it is based in part on results of flux‐adjusted models. Also, reducing or eliminating flux adjustments would probably do little to improve simulations of temperature variability.

Comparison of temperature variability in observations and sixteen climate model simulations

Understanding how much, if any, of observed climate changes are anthropogenic depends upon understanding the magnitude and spatial patterns of natural climate variability. We have compared simulated surface air temperature (SAT) variability in 16 coupled ocean‐atmosphere‐sea ice climate model simulations to observed temperature variability. The majority of the simulations exhibit excessive air temperature variability over land while simulated temperature variability over oceans is generally too low. The ratio of variability over land to over oceans is too high in all the simulations, relative to observations. We have identified several factors which may contribute to the differences in temperature variability. In particular, many of the models use ”bucket” land surface schemes which produce greater temperature variability over land, due to lower levels of soil moisture, than more realistic land surface schemes produce.