Abstract

Temperature trends in the middle and upper stratosphere are evaluated using measurements from the Stratospheric Sounding Unit (SSU), combined with data from the Aura Microwave Limb Sounder (MLS) and Sounding of the Atmosphere Using Broadband Emission Radiometry (SABER) instruments. Data from MLS and SABER are vertically integrated to approximate the SSU weighting functions and combined with SSU to provide a data record spanning 1979–2015. Vertical integrals are calculated using empirically derived Gaussian weighting functions, which provide improved agreement with high-latitude SSU measurements compared to previously derived weighting functions. These merged SSU data are used to evaluate decadal-scale trends, solar cycle variations, and volcanic effects from the lower to the upper stratosphere. Episodic warming is observed following the volcanic eruptions of El Chichón (1982) and Mt. Pinatubo (1991), focused in the tropics in the lower stratosphere and in high latitudes in the middle and upper stratosphere. Solar cycle variations are centered in the tropics, increasing in amplitude from the lower to the upper stratosphere. Linear trends over 1979–2015 show that cooling increases with altitude from the lower stratosphere (from ~−0.1 to −0.2 K decade⁻¹) to the middle and upper stratosphere (from ~−0.5 to −0.6 K decade⁻¹). Cooling in the middle and upper stratosphere is relatively uniform in latitudes north of about 30°S, but trends decrease to near zero over the Antarctic. Mid- and upper-stratospheric temperatures show larger cooling over the first half of the data record (1979–97) compared to the second half (1998–2015), reflecting differences in upper-stratospheric ozone trends between these periods.

Abstract

Updated and improved satellite retrievals of the temperature of the mid-to-upper troposphere (TMT) are used to address key questions about the size and significance of TMT trends, agreement with model-derived TMT values, and whether models and satellite data show similar vertical profiles of warming. A recent study claimed that TMT trends over 1979 and 2015 are 3 times larger in climate models than in satellite data but did not correct for the contribution TMT trends receive from stratospheric cooling. Here, it is shown that the average ratio of modeled and observed TMT trends is sensitive to both satellite data uncertainties and model–data differences in stratospheric cooling. When the impact of lower-stratospheric cooling on TMT is accounted for, and when the most recent versions of satellite datasets are used, the previously claimed ratio of three between simulated and observed near-global TMT trends is reduced to approximately 1.7. Next, the validity of the statement that satellite data show no significant tropospheric warming over the last 18 years is assessed. This claim is not supported by the current analysis: in five out of six corrected satellite TMT records, significant global-scale tropospheric warming has occurred within the last 18 years. Finally, long-standing concerns are examined regarding discrepancies in modeled and observed vertical profiles of warming in the tropical atmosphere. It is shown that amplification of tropical warming between the lower and mid-to-upper troposphere is now in close agreement in the average of 37 climate models and in one updated satellite record.

Abstract

The National Ice Center relies upon a coupled ice–ocean model called the Polar Ice Prediction System (PIPS) to provide guidance for its 24–120-h sea ice forecasts. Here forecast skill assessments of the sea ice concentration (C) fields from PIPS for the period 1 May 2000–31 May 2002 are presented. Methods of measuring the sea ice forecast skill are adapted from the meteorological literature and applied to locations where the forecast or analysis sea ice fields changed by at least ±5%. The forecast skill referenced to climatology was high (>0.85, relative to a maximum score of 1.0) for all months examined. This is because interannual variability in the climatology, which is used as a reference field, is much greater than the day-to-day variability in the forecast field. The PIPS forecasts were also evaluated against persistence and combined climatological–persistence forecasts. Compared to persistence, the 24-h forecast was found to be skillful (>0.2) for all months studied except during the freeze-up months of December 2000 and January 2001. Relative to the combined reference field, the 24-h forecast was also positive for the non-freeze-up months; however, the skill scores were lower (∼0.1). During the poorly performing freeze-up months, a linear combination of persistence (∼95% weight) and climatology (∼5% weight) appears to provide the best available sea ice forecast.

To examine the less restrictive question of whether PIPS can forecast sea ice concentration changes, independent of the magnitude of the changes, “threat indexes” patterned after methods developed for tornado forecasting were established. Two specific questions were addressed with this technique. The first question is: What is the skill of forecasting locations at which a decrease in sea ice concentration has occurred? The second question is: Does PIPS correctly forecast melt-out regions? Using the more relaxed criterion of a threat index for defining correct forecasts, it was found that PIPS correctly made 24-h forecasts of decreasing sea ice concentration ∼10%–15% of the time (it also correctly forecast increasing sea ice concentration an additional ∼10%–15% of the time). However, PIPS correctly forecast melt-out conditions <5% of the time, suggesting that there may be deficiencies in the PIPS parameterization of marginal ice zone processes and/or uncertainties in the atmospheric–oceanic fields that force PIPS.

Abstract

We compare atmospheric temperature changes in satellite data and in model ensembles performed under phases 5 and 6 of the Coupled Model Intercomparison Project (CMIP5 and CMIP6). In the lower stratosphere, multidecadal stratospheric cooling during the period of strong ozone depletion is smaller in newer CMIP6 simulations than in CMIP5 or satellite data. In the troposphere, however, despite forcing and climate sensitivity differences between the two CMIP ensembles, their ensemble-average global warming over 1979–2019 is very similar. We also examine four properties of tropical behavior governed by basic physical processes. The first three are ratios between trends in water vapor (WV) and trends in sea surface temperature (SST), lower-tropospheric temperature (TLT), and mid- to upper-tropospheric temperature (TMT). The fourth property is the ratio between TMT and SST trends. All four ratios are tightly constrained in CMIP simulations but diverge markedly in observations. Model trend ratios between WV and temperature are closest to observed ratios when the latter are calculated with datasets exhibiting larger tropical warming of the ocean surface and troposphere. For the TMT/SST ratio, model–data consistency depends on the combination of observations used to estimate TMT and SST trends. If model expectations of these four covariance relationships are realistic, our findings reflect either a systematic low bias in satellite tropospheric temperature trends or an overestimate of the observed atmospheric moistening signal. It is currently difficult to determine which interpretation is more credible. Nevertheless, our analysis reveals anomalous covariance behavior in several observational datasets and illustrates the diagnostic power of simultaneously considering multiple complementary variables.

Abstract

Satellite observations are indispensable for weather forecasting, climate change monitoring, and environmental studies. Understanding and quantifying errors and uncertainties associated with satellite observations are essential for hardware calibration, data assimilation, and developing environmental and climate data records. Satellite observation errors can be classified into four categories: measurement, observation operator, representativeness, and preprocessing errors. Current methods for diagnosing observation errors still yield large uncertainties due to these complex errors. When simulating satellite errors, empirical errors are usually used, which do not always accurately represent the truth. We address these challenges by developing an error inventory simulator, the Satellite Error Representation and Realization (SatERR). SatERR can simulate a wide range of observation errors, from instrument measurement errors to model assimilation errors. Most of these errors are based on physical models, including existing and newly developed algorithms. SatERR takes a bottom-up approach: errors are generated from root sources and forward propagate through radiance and science products. This is different from, but complementary to, the top-down approach of current diagnostics, which inversely solves unknown errors. The impact of different errors can be quantified and partitioned, and a ground-truth testbed can be produced to test and refine diagnostic methods. SatERR is a community error inventory, open-source on GitHub, which can be expanded and refined with input from engineers, scientists, and modelers. This debut version of SatERR is centered on microwave sensors, covering traditional large satellites and small satellites operated by NOAA, NASA, and EUMETSAT.

Abstract

Previous work identified an anthropogenic fingerprint pattern in T _AC(x, t), the amplitude of the seasonal cycle of mid- to upper-tropospheric temperature (TMT), but did not explicitly consider whether fingerprint identification in satellite T _AC(x, t) data could have been influenced by real-world multidecadal internal variability (MIV). We address this question here using large ensembles (LEs) performed with five climate models. LEs provide many different sequences of internal variability noise superimposed on an underlying forced signal. Despite differences in historical external forcings, climate sensitivity, and MIV properties of the five models, their T _AC(x, t) fingerprints are similar and statistically identifiable in 239 of the 240 LE realizations of historical climate change. Comparing simulated and observed variability spectra reveals that consistent fingerprint identification is unlikely to be biased by model underestimates of observed MIV. Even in the presence of large (factor of 3–4) intermodel and inter-realization differences in the amplitude of MIV, the anthropogenic fingerprints of seasonal cycle changes are robustly identifiable in models and satellite data. This is primarily due to the fact that the distinctive, global-scale fingerprint patterns are spatially dissimilar to the smaller-scale patterns of internal T _AC(x, t) variability associated with the Atlantic multidecadal oscillation and El Niño–Southern Oscillation. The robustness of the seasonal cycle detection and attribution results shown here, taken together with the evidence from idealized aquaplanet simulations, suggest that basic physical processes are dictating a common pattern of forced T _AC(x, t) changes in observations and in the five LEs. The key processes involved include GHG-induced expansion of the tropics, lapse-rate changes, land surface drying, and sea ice decrease.

The NCEP Climate Forecast System Reanalysis (CFSR) was completed for the 31-yr period from 1979 to 2009, in January 2010. The CFSR was designed and executed as a global, high-resolution coupled atmosphere–ocean–land surface–sea ice system to provide the best estimate of the state of these coupled domains over this period. The current CFSR will be extended as an operational, real-time product into the future. New features of the CFSR include 1) coupling of the atmosphere and ocean during the generation of the 6-h guess field, 2) an interactive sea ice model, and 3) assimilation of satellite radiances by the Gridpoint Statistical Interpolation (GSI) scheme over the entire period. The CFSR global atmosphere resolution is ~38 km (T382) with 64 levels extending from the surface to 0.26 hPa. The global ocean's latitudinal spacing is 0.25° at the equator, extending to a global 0.5° beyond the tropics, with 40 levels to a depth of 4737 m. The global land surface model has four soil levels and the global sea ice model has three layers. The CFSR atmospheric model has observed variations in carbon dioxide (CO₂) over the 1979–2009 period, together with changes in aerosols and other trace gases and solar variations. Most available in situ and satellite observations were included in the CFSR. Satellite observations were used in radiance form, rather than retrieved values, and were bias corrected with “spin up” runs at full resolution, taking into account variable CO₂ concentrations. This procedure enabled the smooth transitions of the climate record resulting from evolutionary changes in the satellite observing system.

CFSR atmospheric, oceanic, and land surface output products are available at an hourly time resolution and a horizontal resolution of 0.5° latitude × 0.5° longitude. The CFSR data will be distributed by the National Climatic Data Center (NCDC) and NCAR. This reanalysis will serve many purposes, including providing the basis for most of the NCEP Climate Prediction Center's operational climate products by defining the mean states of the atmosphere, ocean, land surface, and sea ice over the next 30-yr climate normal (1981–2010); providing initial conditions for historical forecasts that are required to calibrate operational NCEP climate forecasts (from week 2 to 9 months); and providing estimates and diagnoses of the Earth's climate state over the satellite data period for community climate research.

Preliminary analysis of the CFSR output indicates a product that is far superior in most respects to the reanalysis of the mid-1990s. The previous NCEP–NCAR reanalyses have been among the most used NCEP products in history; there is every reason to believe the CFSR will supersede these older products both in scope and quality, because it is higher in time and space resolution, covers the atmosphere, ocean, sea ice, and land, and was executed in a coupled mode with a more modern data assimilation system and forecast model.

Abstract

—J. BLUNDEN, T. BOYER, AND E. BARTOW-GILLIES

Earth’s global climate system is vast, complex, and intricately interrelated. Many areas are influenced by global-scale phenomena, including the “triple dip” La Niña conditions that prevailed in the eastern Pacific Ocean nearly continuously from mid-2020 through all of 2022; by regional phenomena such as the positive winter and summer North Atlantic Oscillation that impacted weather in parts the Northern Hemisphere and the negative Indian Ocean dipole that impacted weather in parts of the Southern Hemisphere; and by more localized systems such as high-pressure heat domes that caused extreme heat in different areas of the world. Underlying all these natural short-term variabilities are long-term climate trends due to continuous increases since the beginning of the Industrial Revolution in the atmospheric concentrations of Earth’s major greenhouse gases.

In 2022, the annual global average carbon dioxide concentration in the atmosphere rose to 417.1±0.1 ppm, which is 50% greater than the pre-industrial level. Global mean tropospheric methane abundance was 165% higher than its pre-industrial level, and nitrous oxide was 24% higher. All three gases set new record-high atmospheric concentration levels in 2022.

Sea-surface temperature patterns in the tropical Pacific characteristic of La Niña and attendant atmospheric patterns tend to mitigate atmospheric heat gain at the global scale, but the annual global surface temperature across land and oceans was still among the six highest in records dating as far back as the mid-1800s. It was the warmest La Niña year on record. Many areas observed record or near-record heat. Europe as a whole observed its second-warmest year on record, with sixteen individual countries observing record warmth at the national scale. Records were shattered across the continent during the summer months as heatwaves plagued the region. On 18 July, 104 stations in France broke their all-time records. One day later, England recorded a temperature of 40°C for the first time ever. China experienced its second-warmest year and warmest summer on record. In the Southern Hemisphere, the average temperature across New Zealand reached a record high for the second year in a row. While Australia’s annual temperature was slightly below the 1991–2020 average, Onslow Airport in Western Australia reached 50.7°C on 13 January, equaling Australia's highest temperature on record.

While fewer in number and locations than record-high temperatures, record cold was also observed during the year. Southern Africa had its coldest August on record, with minimum temperatures as much as 5°C below normal over Angola, western Zambia, and northern Namibia. Cold outbreaks in the first half of December led to many record-low daily minimum temperature records in eastern Australia.

The effects of rising temperatures and extreme heat were apparent across the Northern Hemisphere, where snow-cover extent by June 2022 was the third smallest in the 56-year record, and the seasonal duration of lake ice cover was the fourth shortest since 1980. More frequent and intense heatwaves contributed to the second-greatest average mass balance loss for Alpine glaciers around the world since the start of the record in 1970. Glaciers in the Swiss Alps lost a record 6% of their volume. In South America, the combination of drought and heat left many central Andean glaciers snow free by mid-summer in early 2022; glacial ice has a much lower albedo than snow, leading to accelerated heating of the glacier. Across the global cryosphere, permafrost temperatures continued to reach record highs at many high-latitude and mountain locations.

In the high northern latitudes, the annual surface-air temperature across the Arctic was the fifth highest in the 123-year record. The seasonal Arctic minimum sea-ice extent, typically reached in September, was the 11th-smallest in the 43-year record; however, the amount of multiyear ice—ice that survives at least one summer melt season—remaining in the Arctic continued to decline. Since 2012, the Arctic has been nearly devoid of ice more than four years old.

In Antarctica, an unusually large amount of snow and ice fell over the continent in 2022 due to several landfalling atmospheric rivers, which contributed to the highest annual surface mass balance, 15% to 16% above the 1991–2020 normal, since the start of two reanalyses records dating to 1980. It was the second-warmest year on record for all five of the long-term staffed weather stations on the Antarctic Peninsula. In East Antarctica, a heatwave event led to a new all-time record-high temperature of −9.4°C—44°C above the March average—on 18 March at Dome C. This was followed by the collapse of the critically unstable Conger Ice Shelf. More than 100 daily low sea-ice extent and sea-ice area records were set in 2022, including two new all-time annual record lows in net sea-ice extent and area in February.

Across the world’s oceans, global mean sea level was record high for the 11th consecutive year, reaching 101.2 mm above the 1993 average when satellite altimetry measurements began, an increase of 3.3±0.7 over 2021. Globally-averaged ocean heat content was also record high in 2022, while the global sea-surface temperature was the sixth highest on record, equal with 2018. Approximately 58% of the ocean surface experienced at least one marine heatwave in 2022. In the Bay of Plenty, New Zealand’s longest continuous marine heatwave was recorded.

A total of 85 named tropical storms were observed during the Northern and Southern Hemisphere storm seasons, close to the 1991–2020 average of 87. There were three Category 5 tropical cyclones across the globe—two in the western North Pacific and one in the North Atlantic. This was the fewest Category 5 storms globally since 2017. Globally, the accumulated cyclone energy was the lowest since reliable records began in 1981. Regardless, some storms caused massive damage. In the North Atlantic, Hurricane Fiona became the most intense and most destructive tropical or post-tropical cyclone in Atlantic Canada’s history, while major Hurricane Ian killed more than 100 people and became the third costliest disaster in the United States, causing damage estimated at $113 billion U.S. dollars. In the South Indian Ocean, Tropical Cyclone Batsirai dropped 2044 mm of rain at Commerson Crater in Réunion. The storm also impacted Madagascar, where 121 fatalities were reported.

As is typical, some areas around the world were notably dry in 2022 and some were notably wet. In August, record high areas of land across the globe (6.2%) were experiencing extreme drought. Overall, 29% of land experienced moderate or worse categories of drought during the year. The largest drought footprint in the contiguous United States since 2012 (63%) was observed in late October. The record-breaking megadrought of central Chile continued in its 13th consecutive year, and 80-year record-low river levels in northern Argentina and Paraguay disrupted fluvial transport. In China, the Yangtze River reached record-low values. Much of equatorial eastern Africa had five consecutive below-normal rainy seasons by the end of 2022, with some areas receiving record-low precipitation totals for the year. This ongoing 2.5-year drought is the most extensive and persistent drought event in decades, and led to crop failure, millions of livestock deaths, water scarcity, and inflated prices for staple food items.

In South Asia, Pakistan received around three times its normal volume of monsoon precipitation in August, with some regions receiving up to eight times their expected monthly totals. Resulting floods affected over 30 million people, caused over 1700 fatalities, led to major crop and property losses, and was recorded as one of the world’s costliest natural disasters of all time. Near Rio de Janeiro, Brazil, Petrópolis received 530 mm in 24 hours on 15 February, about 2.5 times the monthly February average, leading to the worst disaster in the city since 1931 with over 230 fatalities.

On 14–15 January, the Hunga Tonga-Hunga Ha'apai submarine volcano in the South Pacific erupted multiple times. The injection of water into the atmosphere was unprecedented in both magnitude—far exceeding any previous values in the 17-year satellite record—and altitude as it penetrated into the mesosphere. The amount of water injected into the stratosphere is estimated to be 146±5 Terragrams, or ∼10% of the total amount in the stratosphere. It may take several years for the water plume to dissipate, and it is currently unknown whether this eruption will have any long-term climate effect.