Geoengineering with stratospheric sulfate aerosols can, to some extent, be designed to achieve different climate objectives. Here we use the state‐of‐the‐art Community Earth System Model, version 1, ...with the Whole Atmosphere Community Climate Model as its atmospheric component (CESM1(WACCM)), to compare surface climate and stratospheric effects of two geoengineering strategies. In one, SO2 is injected into the tropical lower stratosphere at the equator to keep global mean temperature nearly constant under an RCP8.5 scenario, as has been commonly simulated in previous studies. In another, the Geoengineering Large Ensemble (GLENS), SO2 is injected into the lower stratosphere at four different locations (30°N/S and 15°N/S) to keep global mean temperature, the interhemispheric temperature gradient, and the equator‐to‐pole temperature gradient nearly unchanged. Both simulations are effective at offsetting changes in global mean temperature and the interhemispheric temperature gradient that result from increased greenhouse gases, but only GLENS fully offsets changes in the equator‐to‐pole temperature gradient. GLENS results in a more even aerosol distribution, whereas equatorial injection tends to result in an aerosol peak in the tropics. Moreover, GLENS requires less total injection than in the equatorial case due to different spatial distributions of the aerosols. Many other aspects of surface climate changes, including precipitation and sea ice coverage, also show reduced changes in GLENS as compared to equatorial injection. Stratospheric changes, including heating, circulation, and effects on the quasi‐biennial oscillation are greatly reduced in GLENS as compared to equatorial injection.
Key Points
Tropospheric and stratospheric side effects of equatorial solar geoengineering are reduced if nonequatorial injections are applied
Some residual temperature effects from equatorial injection can be offset with multiple injection locations
There are still residuals in surface climate and the stratosphere that cannot be offset with four injection locations
Abstract Subseasonal prediction fills the gap between weather forecasts and seasonal outlooks. There is evidence that predictability on subseasonal timescales comes from a combination of atmosphere, ...land, and ocean initial conditions. Predictability from the land is often attributed to slowly varying changes in soil moisture and snowpack, while predictability from the ocean is attributed to sources such as the El Niño Southern Oscillation. Here we use a set of subseasonal reforecast experiments with CESM2 to quantify the respective roles of atmosphere, land, and ocean initial conditions on subseasonal prediction skill over land. These reveal that the majority of prediction skill for global surface temperature in weeks 3–4 comes from the atmosphere, while ocean initial conditions become important after week 4, especially in the Tropics. In the CESM2 subseasonal prediction system, the land initial state does not contribute to surface temperature prediction skill in weeks 3–6 and climatological land conditions lead to higher skill, disagreeing with our current understanding. However, land-atmosphere coupling is important in week 1. Subseasonal precipitation prediction skill also comes primarily from the atmospheric initial condition, except for the Tropics, where after week 4 the ocean state is more important.
Previous observational studies have found a persistent maximum in stratospheric water vapor (SWV) in the upper troposphere lower stratosphere (UTLS) confined by the upper‐level anticyclone over the ...Asian summer monsoon region. This study investigates the simulation of SWV in the Community Earth System Model, version 1 with the Whole Atmosphere Community Climate Model as its atmospheric component CESM1(WACCM). CESM1(WACCM) generally tends to simulate a SWV maximum over the central Pacific Ocean, but this bias is largely improved in the high vertical resolution version. The high vertical resolution model with increased vertical layers in the UTLS is found to have a less stratified UTLS over the central Pacific Ocean compared with the low vertical resolution model. It therefore simulates a steepened potential vorticity gradient over the central Pacific Ocean that better closes the upper‐level anticyclone and confines the SWV within the enhanced transport barrier.
Key Points
The discrepancies in simulating SWV over the Asian summer monsoon region are commonly found in WACCM experiments
The UTLS temperature over the central Pacific Ocean is crucial in confining the SWV within the Asian summer monsoon anticyclone
Model with high vertical resolution resolves the UTLS temperature more accurately which better confines the SWV within the transport barrier
Previous observational studies have found a persistent maximum in stratospheric water vapor (SWV) in the upper troposphere lower stratosphere (UTLS) confined by the upper-level anticyclone over the ...Asian summer monsoon region. This study investigates the simulation of SWV in the Whole Atmosphere Community Climate Model (WACCM). WACCM generally tends to simulate a SWV maximum over the central Pacific Ocean, but this bias is largely improved in the high vertical resolution version. The high vertical resolution model with increased vertical layers in the UTLS is found to have a less stratified UTLS over the central Pacific Ocean compared with the low vertical resolution model. It therefore simulates a steepened PV gradient over the central Pacific Ocean that better closes the upper-level anticyclone and confines the SWV within the enhanced transport barrier.
It is a daunting challenge to conduct initialized hindcasts with enough ensemble members and associated start years to form a drifted climatology from which to compute the anomalies necessary to ...quantify the skill of the hindcasts when compared to observations. This limits the ability to experiment with case studies and other applications where only a few initial years are needed. Here we run a set of hindcasts with CESM1 and E3SMv1 using two different initialization methods for a limited set of start years and use the respective uninitialized free-running historical simulations to form the model climatologies. Since the drifts from the observed initial states in the hindcasts toward the uninitialized model state are large and rapid, after a few years the drifted initialized models approach the uninitialized model climatological errors. Therefore, hindcasts from the limited start years can use the uninitialized climatology to represent the drifted model states after about lead year 3, providing a means to compute forecast anomalies in the absence of a large hindcast sample. There is comparable skill for predicting spatial patterns of multi-year Pacific sea surface temperature anomalies in the domain of the Interdecadal Pacific Oscillation using this method compared to the conventional methodology with a large hindcast data set, though there is a model dependence to the drifts in the two initialization methods.
Sudden stratospheric warmings (SSWs) are an extreme weather event with impacts on the ionosphere and on tropospheric weather and predictability. The mechanisms governing their formation remain ...elusive, despite their deterministic predictability at nearly 2 weeks. This study uses high resolution CESM2 (WACCM6) subseasonal reforecasts to examine the dynamics that differentiate successful and unsuccessful SSW predictions. Successful reforecasts are generally initialized with a weaker stratospheric jet. However, the basic relationships between jet deceleration, wave drag, and the residual mean angular momentum flux do not fundamentally differ between successful and unsuccessful reforecasts. Instead, the projection of the residual circulation onto a weakened jet produces a weaker angular momentum flux, which leads to a more rapid erosion of the jet as the residual circulation cannot effectively balance the sustained wave drag. This information could be used to develop forecasting practices that could probe the likelihood of SSWs at longer timescales.
Plain Language Summary
Sudden stratospheric warmings (SSWs) are rapid breakdowns of the stratospheric polar vortex. The sudden change in winds eventually reaches the surface and creates predictable weather patterns, leading to improved weather forecasts. SSWs are driven by turbulently breaking atmospheric waves, sort of like waves breaking on a beach. By looking at forecasts of different SSWs over the past 20 years, this study shows that there is an important feedback that helps these breaking waves destroy the vortex. When the vortex weakens slightly due to breaking waves, the vortex becomes less efficient at rebuilding itself. If this happens frequently enough, it amplifies the effect of the waves and helps them destroy the vortex. It may be possible to encourage this feedback in weather forecasts to better determine the likelihood of a sudden warming occurring.
Key Points
In WACCM6, sudden stratospheric warming (SSW) prediction is improved when reforecasts are initialized with a weak stratospheric jet
A weaker jet reduces the angular momentum transport by the residual circulation, maintaining the dynamical cascade to a sudden warming
Simple forecasting practices could better initiate this feedback in WACCM6 and potentially improve SSW prediction
Abstract
There is a growing demand for understanding sources of predictability on subseasonal to seasonal (S2S) time scales. Predictability at subseasonal time scales is believed to come from ...processes varying slower than the atmosphere such as soil moisture, snowpack, sea ice, and ocean heat content. The stratosphere as well as tropospheric modes of variability can also provide predictability at subseasonal time scales. However, the contributions of the above sources to S2S predictability are not well quantified. Here we evaluate the subseasonal prediction skill of the Community Earth System Model, version 1 (CESM1), in the default version of the model as well as a version with the improved representation of stratospheric variability to assess the role of an improved stratosphere on prediction skill. We demonstrate that the subseasonal skill of CESM1 for surface temperature and precipitation is comparable to that of operational models. We find that a better-resolved stratosphere improves stratospheric but not surface prediction skill for weeks 3–4.
Abstract
This study focuses on assessing the representation and predictability of North American weather regimes, which are persistent large-scale atmospheric patterns, in a set of initialized ...subseasonal reforecasts created using the Community Earth System Model, version 2 (CESM2). The
k
-means clustering was used to extract four key North American (10°–70°N, 150°–40°W) weather regimes within ERA5 reanalysis, which were used to interpret CESM2 subseasonal forecast performance. Results show that CESM2 can recreate the climatology of the four main North American weather regimes with skill but exhibits biases during later lead times with overoccurrence of the West Coast high regime and underoccurrence of the Greenland high and Alaskan ridge regimes. Overall, the West Coast high and Pacific trough regimes exhibited higher predictability within CESM2, partly related to El Niño. Despite biases, several reforecasts were skillful and exhibited high predictability during later lead times, which could be partly attributed to skillful representation of the atmosphere from the tropics to extratropics upstream of North America. The high predictability at the subseasonal time scale of these case-study examples was manifested as an “ensemble realignment,” in which most ensemble members agreed on a prediction despite ensemble trajectory dispersion during earlier lead times. Weather regimes were also shown to project distinct temperature and precipitation anomalies across North America that largely agree with observational products. This study further demonstrates that unsupervised learning methods can be used to uncover sources and limits of subseasonal predictability, along with systematic biases present in numerical prediction systems.
Significance Statement
North American weather regimes are large-scale atmospheric patterns that can persist for several days. Their skillful subseasonal (2 weeks or greater) prediction can provide valuable lead time to prepare for temperature and precipitation anomalies that can stress energy and water resources. The purpose of this study was to assess the climatological representation and subseasonal predictability of four key North American weather regimes using a research subseasonal prediction system and clustering analysis. We found that the Pacific trough and West Coast high regimes exhibited higher predictability than other regimes and that skillful representation of conditions across the tropics and extratropics can increase predictability during later lead times. Future work will quantify causal pathways associated with high predictability.
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