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Yushan Qu
,
Shengpeng Wang
,
Zhao Jing
,
Yu Zhang
,
Hong Wang
, and
Lixin Wu

Abstract

Tropical Pacific quasi-decadal (TPQD) climate variability is characterized by quasi-decadal sea surface temperature (SST) variations in the central Pacific (CP). This low-frequency climate variability is suggested to influence extreme regional weather and substantially impact global climate patterns and associated socioeconomics through teleconnections. Previous studies mostly attributed the TPQD climate variability to basin-scale air–sea coupling processes. However, due to the coarse resolution of the majority of the observations and climate models, the role of subbasin-scale processes in modulating the TPQD climate variability is still unclear. Using a long-term high-resolution global climate model, we find that energetic small-scale motions with horizontal scales from tens to hundreds of kilometers (loosely referred to as equatorial submesoscale eddies) act as an important damping effect to retard the TPQD variability. During the positive TPQD events, compound increasing precipitation and warming SST in the equatorial Pacific intensifies the upper ocean stratification and weakens the temperature fronts along the Pacific cold tongue. This suppresses submesoscale eddy growth as well as their associated upward vertical heat transport by inhibiting baroclinic instability (BCI) and frontogenesis; conversely, during the negative TPQD events, the opposite is true. Using a series of coupled global climate models that participated in phase 6 of the Coupled Model Intercomparison Project with different oceanic resolutions, we show that the amplitude of the TPQD variability becomes smaller as the oceanic resolution becomes finer, providing evidence for the impacts of submesoscale eddies on damping the TPQD variability. Our study suggests that explicitly simulating equatorial submesoscale eddies is necessary for gaining a more robust understanding of low-frequency tropical climate variability.

Significance Statement

Submesoscale ocean eddies inhibit the development of quasi-decadal climate variability in the equatorial central Pacific, according to a high-resolution global climate simulation.

Open access
George J. Huffman
,
Robert F. Adler
,
Ali Behrangi
,
David T. Bolvin
,
Eric J. Nelkin
,
Guojun Gu
, and
Mohammad Reza Ehsani

Abstract

The Global Precipitation Climatology Project (GPCP) Version 3.2 Precipitation Analysis provides globally complete analyses of surface precipitation on a 0.5° × 0.5° latitude–longitude grid at both monthly and daily time scales, covering from 1983 to the present and from June 2000 to the present, respectively. These merged products continue the GPCP heritage of incorporating precipitation estimates from low-orbit satellite microwave data, geosynchronous-orbit satellite infrared data, sounder-based estimates, and surface rain gauge observations emphasizing the strengths of various inputs and striving for time and space homogeneity. Furthermore, these analyses incorporate modern algorithms, refined intercalibrations among sensors, climatologies of recent high-quality satellite precipitation data, and fine-scale multisatellite estimates. New data fields have been introduced to better characterize the precipitation, including the fraction of the precipitation that is liquid (rain) in both the monthly and daily products, and a quality index for the monthly product. Compared to the operational GPCP Version 2.3 Monthly, the Version 3.2 Monthly product provides a more reasonable climatology in the Southern Ocean and increases the estimated global average precipitation by about 4.5%, which is similar to estimates from recent global water budget assessments. Global and regional trends for 1983–2020 with this new Monthly dataset are very similar to those computed from Version 2.3. Compared to the operational One-Degree Daily (Version 1.3) product, the new Version 3.2 Daily is designed to better represent the histogram of precipitation rates, particularly at high values and shifts the start of less-certain high-latitude estimates from 40° to 58° latitude in each hemisphere.

Significance Statement

Studies of Earth’s climate require long-term global datasets based on observations to show how the climate functions and to validate numerical climate models. This study describes an important upgrade to the monthly and daily precipitation (rain and snow) products computed by the Global Precipitation Climatology Project. We use modern analysis schemes, add new sources of data, and deliver results on a finer-scale 0.5° × 0.5° latitude–longitude grid [roughly 55 km (34 mi) on a side at the equator]. The new data show improved agreement with other studies and depict more reasonable behavior in the Southern Ocean. The daily product shows improved estimates of how often different intensities of precipitation occur around the world, particularly the high amounts that drive floods and landslides.

Open access
Yongxiao Liang
,
Nathan P. Gillett
, and
Adam H. Monahan

Abstract

Physically based observational constraint methods can effectively reduce uncertainty in global warming projections but have not been widely applied at regional scales. We first develop and apply multivariate linear regression models for constraining projections of surface air temperature averaged over subcontinental regions in the extratropical Northern Hemisphere, based on a set of potential constraints including climatological metrics derived from tropical and subtropical low-level cloud and global average past warming trend, as well as a set of regional climate metrics previously used in the literature. We evaluate the performance of the multivariate linear regression models based on cross-validated tests using output from phases 5 and 6 of the Coupled Model Intercomparison Projects (CMIP). We find that linear regression models using global-scale low-cloud metrics alone perform more robustly than linear regression models using the past global mean warming trend or regional climate metrics as constraints. These results, while favoring global constraints over the set of regional constraints considered, do not preclude the existence of even better regional constraints for particular regions. Through model-based cross-validation, the projections constrained using low-level cloud metrics exhibit more accurate best estimate projections, narrower uncertainty ranges, and more reliable uncertainty estimates in most Northern Hemisphere regions when compared with unconstrained projections. Application of the approach to climate projections based on both Shared Socioeconomic Pathway (SSP) 1-2.6 and SSP5-8.5 using observed low-cloud metrics results in considerably narrower 5%–95% uncertainty ranges of twenty-first-century warming over subcontinental Northern Hemisphere land regions compared to unconstrained projections.

Open access
Frerk Pöppelmeier
,
Fortunat Joos
, and
Thomas F. Stocker

Abstract

Understanding climate variability from millennial to glacial–interglacial time scales remains challenging due to the complex and nonlinear feedbacks between ice, ocean, sediments, biosphere, and atmosphere. Complex climate models generally struggle to dynamically and comprehensively simulate such long time periods as a result of the large computational costs. Here, we therefore coupled a dynamical ice sheet model to the Bern3D Earth system model of intermediate complexity, which allows for simulating multiple glacial–interglacial cycles. The performance of the model is first validated against modern observations and its response to abrupt perturbations, such as atmospheric CO2 changes and North Atlantic freshwater hosing, is investigated. To further test the fully coupled model, the climate evolution over the entire last glacial cycle is explored in a transient simulation forced by variations in the orbital configuration and greenhouse gases and aerosols. The model simulates global mean surface temperature in fair agreement with reconstructions, exhibiting a gradual cooling trend since the last interglacial that is interrupted by two more rapid cooling events during the early Marine Isotope Stage (MIS) 4 and Last Glacial Maximum (LGM). Simulated Northern Hemispheric ice sheets show pronounced variability on orbital time scales, and ice volume more than doubles from MIS3 to the LGM in good agreement with recent sea level reconstructions. At the LGM, the Atlantic overturning has a strength of about 14 Sv (1 Sv ≡ 106 m3 s−1), which is a reduction by about one-quarter compared to the preindustrial. We thus demonstrate that the new coupled model is able to simulate large-scale aspects of glacial–interglacial cycles.

Open access
Cameron Dong
,
Yannick Peings
, and
Gudrun Magnusdottir

Abstract

In this study, we analyze drivers of non–El Niño–Southern Oscillation (ENSO) precipitation variability in the Southwest United States (SWUS) and the influence of the atmospheric basic state, using atmosphere-only and ocean–atmosphere coupled simulations from the Community Earth System Model version 2 (CESM2) large ensemble. A cluster analysis identifies three main wave trains associated with non-ENSO SWUS precipitation in the experiments: a meridional ENSO-type wave train, an arching Pacific–North American-type (PNA) wave train, and a circumglobal zonal wave train. The zonal wave train cluster frequency differs between models and ENSO phase, with decreased frequency during El Niño and the coupled runs, and increased frequency during La Niña and the atmosphere-only runs. This is consistent with an El Niño–like bias of the atmospheric circulation in the coupled model, with strengthened subtropical westerlies in the central and eastern North Pacific that cause a retraction of the waveguide in the midlatitude eastern North Pacific. As such, zonal wave trains from the East Asian jet stream (EAJS) are more likely to be diverted southward in the east Pacific in the coupled large ensemble, with a consequently smaller role in driving SWUS precipitation variability. This study illustrates the need to reduce model biases in the background flow, particularly relating to the jet stream, in order to accurately capture the role of large-scale teleconnections in driving SWUS precipitation variability and improve future forecasting capabilities.

Open access
Kevin Schwarzwald
,
Richard Seager
,
Mingfang Ting
, and
Alessandra Giannini

Abstract

The societies of the coastal regions of the Greater Horn of Africa (GHA) experience two distinct rainy seasons: the generally wetter “long” rains in the boreal spring and the generally drier “short” rains in the boreal fall. The GHA rainfall climatology is unique for its latitude in both its aridity and for the dynamical differences between its two rainy seasons. This study explains the drivers of the rainy seasons through the climatology of moist static stability, estimated as the difference between surface moist static energy hs and midtropospheric saturation moist static energy h * . In areas and at times when this difference, h s h * , is higher, rainfall is more frequent and more intense. However, even during the rainy seasons, h s h * < 0 on average and the atmosphere remains largely stable, in line with the GHA’s aridity. The seasonal cycle of h s h * , to which the unique seasonal cycles of surface humidity, surface temperature, and midtropospheric temperature all contribute, helps explain the double-peaked nature of the regional hydroclimate. Despite tropospheric temperature being relatively uniform in the tropics, even small changes in h * can have substantial impacts on instability; for example, during the short rains, the annual minimum in GHA h * lowers the threshold for convection and allows for instability despite surface humidity anomalies being relatively weak. This h s h * framework can help identify the drivers of interannual variability in GHA mean rainfall or diagnose the origin of biases in climate model simulations of the regional climate.

Open access
Jonah K. Shaw
and
Jennifer E. Kay

Abstract

Most observed patterns of recent Arctic surface warming and sea ice loss lie outside of unforced internal climate variability. In contrast, human influence on related changes in outgoing longwave radiation has not been assessed. Outgoing longwave radiation captures the flow of thermal energy from the surface through the atmosphere to space, making it an essential indicator of Arctic change. Furthermore, satellites have measured pan-Arctic radiation for two decades while surface temperature observations remain spatially and temporally sparse. Here, two climate model initial-condition large ensembles and satellite observations are used to investigate when and why twenty-first-century Arctic outgoing longwave radiation changes emerge from unforced internal climate variability. Observationally, outgoing longwave radiation changes from 2001 to 2021 are within the range of unforced internal variability for all months except October. The model-predicted timing of Arctic longwave radiation emergence varies throughout the year. Specifically, fall emergence occurs a decade earlier than spring emergence. These large emergence timing differences result from seasonally dependent sea ice loss and surface warming. The atmosphere and clouds then widen these seasonal differences by delaying emergence more in the spring and winter than in the fall. Finally, comparison of the two ensembles shows that more sea ice and a more transparent atmosphere during the melt season led to an earlier emergence of forced longwave radiation changes. Overall, these findings demonstrate that attributing changes in Arctic outgoing longwave radiation to human influence requires understanding the seasonality of both forced change and internal climate variability.

Open access
J. K. Eischeid
,
M. P. Hoerling
,
X.-W. Quan
,
A. Kumar
,
J. Barsugli
,
Z. M. Labe
,
K. E. Kunkel
,
C. J. Schreck III
,
D. R. Easterling
,
T. Zhang
,
J. Uehling
, and
X. Zhang

Abstract

A cooling trend in summer (May–August) daytime temperatures since the mid-twentieth century over the central United States contrasts with strong warming of the western and eastern United States. Prior studies based on data through 1999 suggested that this so-called warming hole arose mainly from internal climate variability and thus would likely disappear. Yet it has prevailed for two more decades, despite accelerating global warming, compelling reexamination of causes that in addition to natural variability could include anthropogenic aerosol–induced cooling, hydrologic cycle intensification by greenhouse gas increases, and land use change impacts. Here we present evidence for the critical importance of hydrologic cycle change resulting from ocean–atmosphere drivers. Observational analysis reveals that the warming hole’s persistence is consistent with unusually high summertime rainfall over the region during the first decades of the twenty-first century. Comparative analysis of large ensembles from four different climate models demonstrates that rainfall trends since the mid-twentieth century as large as observed can arise (although with low probability) via internal atmospheric variability alone, which induce warming-hole-like patterns over the central United States. In addition, atmosphere-only model experiments reveal that observed sea surface temperature changes since the mid-twentieth century have also favored central U.S cool/wet conditions during the early twenty-first century. We argue that this latter effect is symptomatic of external radiative forcing influences, which, via constraints on ocean warming patterns, have likewise contributed to persistence of the U.S. warming hole in roughly equal proportion to contributions by internal variability. These results have important ramifications for attribution of extreme events and predicting risks of record-breaking heat waves in the region.

Significance Statement

Our paper makes a significant contribution to analysis of a cooling trend in summer (May–August) daytime temperatures since the mid-twentieth century over the central United States, contrasting with strong warming over the remainder of the United States and having important ramifications for assigning cause to and predicting record-breaking heat waves in the region. Observations and model simulations reveal the critical importance of hydrologic cycle change resulting from ocean–atmosphere impacts. Precipitation has increased substantially over the region as a result of atmospheric circulation trends consisting of generally lower pressure and cooler air advection into the region. The persistence of this pattern of increased rainfall/lower temperatures is likely due to near-equal contributions of external forcing (climate change) and internal climate variability.

Open access
Chanud N. Yasanayake
,
Benjamin F. Zaitchik
, and
Anand Gnanadesikan

Abstract

For the tropical country of Sri Lanka, subseasonal variability in precipitation is both ecologically and societally relevant, influencing agricultural yields, natural hazard risk, energy production, and disease incidence. The primary driver of this subseasonal precipitation variability is the Madden–Julian oscillation (MJO). Here we investigate this influence on Sri Lankan precipitation across seasons, describing MJO-associated precipitation patterns and exploring the potential for MJO-informed subseasonal forecasts. We do so using 40-yr satellite-derived records of precipitation with high spatial resolution (from CHIRPS v2.0) and related meteorological and atmospheric fields (from ERA5 and MERRA-2). We find a direct MJO influence on precipitation corresponding to propagation of the MJO’s convectively active region and suppressed region near Sri Lanka, with the strength and spatial patterns of this influence differing across seasons. There are particularly strong impacts in the second intermonsoon (SIM; October–November) and southwest monsoon (SWM; May–September) seasons. During SIM the impacts are island-wide, but strongest in the northeast. During the SWM the absolute impacts are localized to the southwest, but the relative impacts (i.e., relative to precipitation climatology) are fairly uniform across the island. Moreover, we find significant associations between MJO phase and Sri Lankan precipitation at time scales of up to several weeks. Notably, these associations are stronger when using the OLR-based MJO index (OMI) rather than the more commonly used real-time multivariate MJO index (RMM). While the MJO associations we describe here arise from a highly simplified forecasting scheme, they provide a foundation and impetus for developing a more complete, MJO-informed precipitation forecast method.

Significance Statement

Rainfall variability at the subseasonal (weeks–months) time scale is critical to societal well-being, given its fundamental importance for agriculture, flood risk, hydropower generation, and disease incidence. Our work describes how such rainfall variability in Sri Lanka is impacted by the Madden–Julian oscillation, in which a region of enhanced rainfall and cloudiness, paired with a region of decreased rainfall and cloudiness, circles the globe every 30–60 days. Our results suggest that its influence on Sri Lankan rainfall may be strong enough that incorporating knowledge of the Madden–Julian oscillation into forecasts can improve the accuracy of rainfall prediction for Sri Lanka. Future work should develop a more comprehensive forecast method to assess viability in real-world forecasting scenarios.

Open access
Daniela Granato-Souza
and
David W. Stahle

Abstract

Recent severe droughts, extreme floods, and increasing differences between seasonal high and low flows on the Amazon River may represent a twenty-first-century increase in the amplitude of the hydrologic cycle over the Amazon Basin. These precipitation and streamflow changes may have arisen from natural ocean–atmospheric variability, deforestation within the drainage basin of the Amazon River, or anthropogenic climate change. Tree-ring reconstructions of wet-season precipitation extremes, substantiated with historical accounts of climate and river levels on the Amazon River and in northeast Brazil found in the Brazilian Digital Library, indicate that the recent river-level extremes on the Amazon may have been equaled or possibly exceeded during the preinstrumental nineteenth century. The “Forgotten Drought” of 1865 was the lowest wet-season rainfall total reconstructed with tree-rings in the eastern Amazon from 1790 to 2016 and appears to have been one of the lowest stream levels observed on the Amazon River during the historical era according to first-hand descriptions by Louis Agassiz, his Brazilian colleague João Martins da Silva Coutinho, and others. Heavy rains and flooding are described during most of the tree-ring-reconstructed wet extremes, including the complete inundation of “First Street” in Santarem, Brazil, in 1859 and the overtopping of the Bittencourt Bridge in Manaus, Brazil, in 1892. These extremes in the tree-ring estimates and historical observations indicate that recent high and low flow anomalies on the Amazon River may not have exceeded the natural variability of precipitation and streamflow during the nineteenth century.

Significance Statement

Proxy tree-ring and historical evidence for precipitation extremes during the preinstrumental nineteenth century indicate that recent floods and droughts on the Amazon River may have not yet exceeded the range of natural hydroclimatic variability.

Open access