An official website of the United States government
Abstract The global seasonal cycle of energy in Earth’s climate system is quantified using observations and reanalyses. After removing long-term trends, net energy entering and exiting the climate system at the top of the atmosphere (TOA) should agree with the sum of energy entering and exiting the ocean, atmosphere, land, and ice over the course of an average year. Achieving such a balanced budget with observations has been challenging. Disagreements have been attributed previously to sparse observations in the high-latitude oceans. However, limiting the local vertical integration of new global ocean heat content estimates to the depth to which seasonal heat energy is stored, rather than integrating to 2000 m everywhere as done previously, allows closure of the global seasonal energy budget within statistical uncertainties. The seasonal cycle of energy storage is largest in the ocean, peaking in April because ocean area is largest in the Southern Hemisphere and the ocean’s thermal inertia causes a lag with respect to the austral summer solstice. Seasonal cycles in energy storage in the atmosphere and land are smaller, but peak in July and September, respectively, because there is more land in the Northern Hemisphere, and the land has more thermal inertia than the atmosphere. Global seasonal energy storage by ice is small, so the atmosphere and land partially offset ocean energy storage in the global integral, with their sum matching time-integrated net global TOA energy fluxes over the seasonal cycle within uncertainties, and both peaking in April.
more »
« less
Seasonal Asymmetries in the Lag between Insolation and Surface Temperature
We analyze the temporal structure of the climatological seasonal cycle in surface air temperature across the globe. We find that, over large regions of Earth, the seasonal cycle of surface temperature departs from an annual harmonic: the duration of fall and spring differ by as much as 2 months. We characterize this asymmetry by the metric ASYM, defined as the phase lag of the seasonal maximum temperature relative to the summer solstice minus the phase lag of the seasonal minimum temperature relative to winter solstice. We present a global analysis of ASYM from weather station data and atmospheric reanalysis and find that ASYM is well represented in the reanalysis. ASYM generally features positive values over land and negative values over the ocean, indicating that spring has a longer duration over the land domain whereas fall has a longer duration over the ocean. However, ASYM also features more positive values over North America compared to Europe and negative values in the polar regions over ice sheets and sea ice. Understanding the root cause of the climatological ASYM will potentially further our understanding of controls on the seasonal cycle of temperature and its future/past changes. We explore several candidate mechanisms to explain the spatial structure of ASYM including 1) modification of the seasonal cycle of surface solar radiation by the seasonal evolution of cloud thickness, 2) differences in the seasonal cycle of the atmospheric boundary layer depth over ocean and over land, and 3) temperature advection by the seasonally evolving atmospheric circulation.
more »
« less
- Award ID(s):
- 1643436
- PAR ID:
- 10143530
- Publisher / Repository:
- American Meteorological Society
- Date Published:
- Journal Name:
- Journal of Climate
- Volume:
- 33
- Issue:
- 10
- ISSN:
- 0894-8755
- Page Range / eLocation ID:
- p. 3921-3945
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Abstract Subseasonal-to-seasonal (S2S) precipitation prediction in boreal spring and summer months, which contains a significant number of high-signal events, is scientifically challenging and prediction skill has remained poor for years. Tibetan Plateau (TP) spring observed surface temperatures show a lag correlation with summer precipitation in several remote regions, but current global land–atmosphere coupled models are unable to represent this behavior due to significant errors in producing observed TP surface temperatures. To address these issues, the Global Energy and Water Exchanges (GEWEX) program launched the “Impact of Initialized Land Temperature and Snowpack on Subseasonal-to-Seasonal Prediction” (LS4P) initiative as a community effort to test the impact of land temperature in high-mountain regions on S2S prediction by climate models: more than 40 institutions worldwide are participating in this project. After using an innovative new land state initialization approach based on observed surface 2-m temperature over the TP in the LS4P experiment, results from a multimodel ensemble provide evidence for a causal relationship in the observed association between the Plateau spring land temperature and summer precipitation over several regions across the world through teleconnections. The influence is underscored by an out-of-phase oscillation between the TP and Rocky Mountain surface temperatures. This study reveals for the first time that high-mountain land temperature could be a substantial source of S2S precipitation predictability, and its effect is probably as large as ocean surface temperature over global “hotspot” regions identified here; the ensemble means in some “hotspots” produce more than 40% of the observed anomalies. This LS4P approach should stimulate more follow-on explorations.more » « less
-
The Arctic Ocean has experienced significant sea ice loss over recent decades, shifting towards a thinner and more mobile seasonal ice regime. However, the impacts of these transformations on the upper ocean dynamics of the biologically productive Pacific Arctic continental shelves remain underexplored. Here, we quantified the summer upper mixed layer depth and analyzed its interannual to decadal evolution with sea ice and atmospheric forcing, using hydrographic observations and model reanalysis from 1996 to 2021. Before 2006, a shoaling summer mixed layer was associated with sea ice loss and surface warming. After 2007, however, the upper mixed layer reversed to a generally deepening trend due to markedly lengthened open water duration, enhanced wind-induced mixing, and reduced ice meltwater input. Our findings reveal a shift in the primary drivers of upper ocean dynamics, with surface buoyancy flux dominant initially, followed by a shift to wind forcing despite continued sea ice decline. These changes in upper ocean structure and forcing mechanisms may have substantial implications for the marine ecosystem, potentially contributing to unusual fall phytoplankton blooms and intensified ocean acidification observed in the past decademore » « less
-
Abstract Atmospheric reanalyses are widely used to estimate the past atmospheric near-surface state over sea ice. They provide boundary conditions for sea ice and ocean numerical simulations and relevant information for studying polar variability and anthropogenic climate change. Previous research revealed the existence of large near-surface temperature biases (mostly warm) over the Arctic sea ice in the current generation of atmospheric reanalyses, which is linked to a poor representation of the snow over the sea ice and the stably stratified boundary layer in the forecast models used to produce the reanalyses. These errors can compromise the employment of reanalysis products in support of polar research. Here, we train a fully connected neural network that learns from remote sensing infrared temperature observations to correct the existing generation of uncoupled atmospheric reanalyses (ERA5, JRA-55) based on a set of sea ice and atmospheric predictors, which are themselves reanalysis products. The advantages of the proposed correction scheme over previous calibration attempts are the consideration of the synoptic weather and cloud state, compatibility of the predictors with the mechanism responsible for the bias, and a self-emerging seasonality and multidecadal trend consistent with the declining sea ice state in the Arctic. The correction leads on average to a 27% temperature bias reduction for ERA5 and 7% for JRA-55 if compared to independent in situ observations from the MOSAiC campaign (respectively, 32% and 10% under clear-sky conditions). These improvements can be beneficial for forced sea ice and ocean simulations, which rely on reanalyses surface fields as boundary conditions. Significance StatementThis study illustrates a novel method based on machine learning for reducing the systematic surface temperature errors that characterize multiple atmospheric reanalyses in sea ice–covered regions of the Arctic under clear-sky conditions. The correction applied to the temperature field is consistent with the local weather and the sea ice and snow conditions, meaning that it responds to seasonal changes in sea ice cover as well as to its long-term decline due to global warming. The corrected reanalysis temperature can be employed to support polar research activities, and in particular to better simulate the evolution of the interacting sea ice and ocean system within numerical models.more » « less
-
Abstract A thermodynamic energy budget analysis is applied to the lowest model level of the ERA5 dataset to investigate the mechanisms that drive the growth and decay of extreme positive surface air temperature (SAT) events. Regional and seasonal variation of the mechanisms are investigated. For each grid point on Earth’s surface, a separate composite analysis is performed for extreme SAT events, which are days when temperature anomaly exceeds the 95th percentile. Among the dynamical terms, horizontal temperature advection of the climatological temperature by the anomalous wind dominates SAT anomaly growth over the extratropics, while nonlinear horizontal temperature advection is a major factor over high-latitude regions and the adiabatic warming is important over major mountainous regions. During the decay period, advection of the climatological temperature by the anomalous wind sustains the warming while nonlinear advection becomes the dominant decay mechanism. Among diabatic heating processes, vertical mixing contributes to the SAT anomaly growth over most locations while longwave radiative cooling hinders SAT anomaly growth, especially over the ocean. However, over arid regions during summer, longwave heating largely contributes to SAT anomaly growth while the vertical mixing dampens the SAT anomaly growth. During the decay period, both longwave cooling and vertical mixing contribute to SAT anomaly decay with more pronounced effects over the ocean and land, respectively. These regional and seasonal characteristics of the processes that drive extreme SAT events can serve as a benchmark for understanding the future behavior of extreme weather.more » « less
