Towards Reliable Probabilistic Time-Series Projections of Global Mean Surface Temperature

Quantifying the risk of global warming exceeding critical targets such as 2.0 ◦ C requires reliable projections of uncertainty as well as best estimates of Global Mean Surface Temperature (GMST). However, uncertainty bands on GMST projections are often calculated heuristically and have several potential shortcomings. In particular, the uncertainty bands shown in IPCC plume projections of GMST are based on the distribution of GMST anomalies from climate model runs and so are strongly determined by model characteristics with little influence from observations of the real-world. Physically motivated time-series approaches are proposed based on fitting energy balance models (EBMs) to climate model outputs and observations in order to constrain future projections. It is shown that EBMs fitted to one forcing scenario will not produce reliable projections when different forcing scenarios are applied. The errors in the EBM projections can be interpreted as arising due to a discrepancy in the effective forcing felt by the model. A simple time-series approach to correcting the projections is proposed based on learning the evolution of the forcing discrepancy so that it can be projected into the future. This approach gives reliable projections of GMST when tested in a perfect model setting. When applied to observations this leads to projected warming of 2.2 ◦ C (1.7 ◦ C to 2.9 ◦ C) in 2100 compared to pre-industrial conditions, 0.4 ◦ C lower than a comparable IPCC anomaly estimate. The probability of staying below the critical 2.0 ◦ C warming target in 2100 more than doubles to 0.28 compared to only 0.11 from a comparably IPCC estimate.

Global Wave Height Slowdown Trend during a Recent Global Warming Slowdown

It has been reported that global warming results in the increase of globally averaged wave heights. What happened to the global-averaged wave heights during the global warming slowdown period (1999–2013)? Using reanalysis products, together with remote sensing and in situ observational data, it was found that the temporal variation pattern of the globally averaged wave heights was similar to the slowdown trend in the increase in global mean surface temperature during the same period. The analysis of the spatial distribution of trends in wave height variation revealed different rates in global oceans: a downward trend in the northeastern Pacific and southern Indian Ocean, and an upward trend in other regions. The decomposition of waves into swells and wind waves demonstrates that swells dominate global wave height variations, which indicates that local sea surface winds indirectly affect the slowdown in the rate of wave height growth.

Conversion of the Knutson et al. (2020) Tropical Cyclone Climate Change Projections to Risk Model Baselines

Knutson et al. (2020) recently published a meta-study that gives multi-model projections for changes in various properties of tropical cyclones under climate change. They considered frequency of tropical cyclones, frequency of very intense tropical cyclones, intensity of tropical cyclones, and total rainfall rate of tropical cyclones. For each of these properties, they reported changes globally and by basin for the six major tropical cylone basins. The changes were presented as the change that would occur with 2 °C warming of global mean surface temperature. These projections are potentially of great use to the tropical cyclone risk modeling community. However, most risk models use temporal baselines, such as the period from 1950 to 2019, and the Knutson et al. results can only be applied to risk models after some steps of adjustment involving past and future global mean surface temperature values. We derive the necessary adjustments and present and discuss some of the resulting projections, for different properties, basins, RCPs and baselines. We find that the results are sensitive to the baseline being used, which implies that users of tropical cyclone risk models need to make sure they clearly understand what baseline their model represents before they adjust the model for climate change. One part of our analysis derives estimates of the implied impact of climate change so far on TC properties, relative to a representative baseline. The computer code we use to calculate the adjustments is available online.

Effects of strongly eddying oceans on multidecadal climate variability in the Community Earth System Model

Climate variability on multidecadal timescales appears to be organized in pronounced patterns with clear expressions in sea surface temperature, such as the Atlantic Multidecadal Variability and the Pacific Decadal Oscillation. These patterns are now well studied both in observations and global climate models and are important in the attribution of climate change. Results from CMIP5 models have indicated large biases in these patterns with consequences for ocean heat storage variability and the global mean surface temperature. In this paper, we use two multi-century Community Earth System Model simulations at coarse (1∘) and fine (0.1∘) ocean model horizontal grid spacing to study the effects of the representation of mesoscale ocean flows on major patterns of multidecadal variability. We find that resolving mesoscale ocean flows both improves the characteristics of the modes of variability with respect to observations and increases the amplitude of the heat content variability in the individual ocean basins. In the strongly eddying model, multidecadal variability increases compared to sub-decadal variability. This shift of spectral power is seen in sea surface temperature indices, basin-scale surface heat fluxes, and the global mean surface temperature. This implies that the current CMIP6 model generation, which predominantly does not resolve the ocean mesoscale, may systematically underestimate multidecadal variability.

Widespread reductions in human labor capacity after 1.5°C warming

Rising temperatures and specific humidity are compounding influences that increase heat stress and reduce safe labor capacity. Here, we evaluate reductions in summertime labor capacity using Large Ensemble experiments from two Earth System Models (ESMs). Vulnerable regions, including the Indian subcontinent, Southeast Asia, and West Africa, are expected to begin experiencing 25% reductions as early as the 2040s. Internal climate variability can cloud the timing of such reductions, with differences in onset between ensemble members exceeding 40 years in high-variability locations. At regional scales, onset times are more certain, with differences between ensemble members typically less than 20 years. We demonstrate the benefits for maintaining human labor capacity associated with limiting the increase in global mean surface temperature (ΔGMST) to 1.5°C, consistent with the Paris Agreement. If ΔGMST exceeds 3.5°C, at least 15% of the global population is projected to experience 50% reductions in summertime labor capacity.

Influence of Dust on Climate during the Mesozoic

<p>Dust in the atmosphere can affect climate by directly absorbing and scattering solar radiation. In present day, most of dust is emitted from the dry regions over North Africa and Arabian Peninsula, it has been shown that they impact on global mean surface temperature, African monsoon, number of tropical cyclones over the Atlantic Ocean, ENSO variability and the strength of Atlantic meridional ocean circulation (AMOC). During the Mesozoic, the continental configuration was very different from the present, the supercontinent Pangea gradually broke up and Atlantic Ocean formed during this time period. On a different continental configuration, the area and location of dry regions may be very different, so the dust emission and atmospheric dust loading is different too. In this work, we use the global Earth system model CESM1.2.2 to examine the influence of dust on climate during the Mesozoic. Specifically, we simulate the dust and climate at two time slices, 250 million years ago (Ma) and 80 Ma. Results show that the atmospheric dust loading in both periods was much higher than that of present day. Such dust induced significant cooling of the surface climate, especially over polar regions.</p>

Simulation and Projection of Global Mean Surface Temperature Using the Empirical Model of Global Climate and Comparison to CMIP6 GCMs

<p>The Empirical Model of Global Climate (EM-GC) (Canty et al., ACP, 2013, McBride et al., ESDD, 2020) is a multiple linear regression, energy balance model that accounts for the natural influences on global mean surface temperature due to ENSO, the 11-year solar cycle, major volcanic eruptions, as well as the anthropogenic influence of greenhouse gases and aerosols and the efficiency of ocean heat uptake. First, we will analyze the human contribution of global warming from 1975-2014 based on the climate record, also known as the attributable anthropogenic warming rate (AAWR). We will compare the values of AAWR found using the EM-GC with values of AAWR from the CMIP6 multi-model ensemble. Preliminary analysis indicates that over the past three decades, the human component of global warming inferred from the CMIP6 GCMs is larger than the human component of warming from the climate record. Second, we will compare values of equilibrium climate sensitivity inferred from the historical climate record to those determined from CMIP6 GCMs using the Gregory et al., GRL, 2004 method. Third, we will use the future abundances of greenhouse gases and aerosols provided by the Shared Socioeconomic Pathways (SSPs) to project future global mean surface temperature change. We will compare the projections of future temperature anomalies from CMIP6 GCMs to those determined by the EM-GC. We will conclude by assessing the probability of the CMIP6 and EM-GC projections of achieving the Paris Agreement target (1.5°C) and upper limit (2.0°C) for several of the SSP scenarios.</p>

Climate variability following large volcanic eruption: CMIP6 model investigation

<p>Volcanic eruptions are an important driver of climate variability. Multiple literature sources have shown that after large explosive eruptions there is a decrease in global mean temperature, caused by an increased amount of stratospheric aerosols which influence the global radiative budget. In this study, we investigate the changes in several climate variables after a volcanic eruption. Using ESMValTool (Earth System Model Evaluation Tool) on an ensemble of historical simulations from CMIP6, such variables as global mean surface temperature (GMST), Arctic sea ice area and Nino 3.4 index were analyzed following the 1883 Krakatoa eruption. While there is a definite decrease in the multi-model mean GMST after the eruption, other indices do not show as prominent change. The reasons for this behavior are under investigation. </p>