Figure S1: Time series of PI global T2m (K) in the LEs, including their mean (mn) and linear tren... more Figure S1: Time series of PI global T2m (K) in the LEs, including their mean (mn) and linear trends (m).
Subseasonal prediction fills the gap between weather forecasts and seasonal outlooks. There is ev... more 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.
Historical observations show that one in two La Niña events last for two consecutive years. Despi... more Historical observations show that one in two La Niña events last for two consecutive years. Despite their outsized impacts on drought, these 2 year La Niña are not predicted on a routine basis. Here we assess their predictability using retrospective forecasts performed with a climate model that simulates realistic multiyear events, as well as with an empirical model based on observed predictors. The skill of the retrospective forecasts allows us to make predictions for the upcoming 2017-2018 boreal winter starting from conditions in November 2015. These 2 year forecasts indicate that the return of La Niña is more likely than not, with a 60% probability based on the climate model and an 80% probability based on the empirical model; the likelihood of El Niño is less than 8% in both cases. These results demonstrate the feasibility of predictions of the duration of La Niña. Plain Language Summary Historical observations show that cold La Niña events in the tropical Pacific often reintensify for a second year. Despite their outsized climate impacts throughout the world, these 2 year La Niña events are not routinely predicted. Our study demonstrates that long-term forecasts of these events are feasible. Moreover, we show increased likelihood of returning La Niña for next boreal winter, a result that is directly relevant for assessing climate risks throughout the world, for instance, over the southern tier of the U.S., where La Niña events create extreme seasonal heat and drought during winter and spring, and the Maritime Continent and Northern Australia, where it causes excess rainfall and flooding.
Synthetic monthly SST anomaly data are constructed using frequency domain analyses of significant... more Synthetic monthly SST anomaly data are constructed using frequency domain analyses of significant principle components derived from reconstructed SST data, in the equatorial Pacific Ocean. The model provides insight into the dominant physical processes contained in each component and retains the relevant sta,tistical properties of the original data, such as the mean, variance, and autocorrelation. Thus, numerous sets of synthetic SST anomaly data can be produced for the equatorial Pacific that are sta,tistically indistinguishable from the original SST anomaly data,. The spatial and temporal SST signatures of the biennial, intradecadal, and decadal pseudoperiodicities are reproduced, including their frequency and duration of occurrence. Specifically, the ENSO warm and cold event signatures recur in the synthetic data, at peak return periods of 2.4, 3.5,5.0, and 6.4 yr. Moreover, the anticipated return period of an extreme ENSO event with a maximum SST anomaly magnitude of I. 7°C is approximately every five warm events and every seven cold events.
There is a growing demand for understanding sources of predictability on subseasonal to seasonal ... more 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 betterresolved stratosphere improves stratospheric but not surface prediction skill for weeks 3-4. SIGNIFICANCE STATEMENT: There is a growing demand in society for understanding sources of predictability on subseasonal to seasonal time scales. In this work we demonstrate that the CESM1 research Earth system model can be utilized as a subseasonal prediction model and show that its subseasonal prediction skill is comparable to that of operational models. We also show that the inclusion of a well-resolved stratosphere does not improve the subseasonal (week 3-4 averaged) forecast of temperature and precipitation at the surface.
Observational analysis has indicated a strong connection between the stratospheric quasi-biennial... more Observational analysis has indicated a strong connection between the stratospheric quasi-biennial oscillation (QBO) and tropospheric Madden-Julian oscillation (MJO), with MJO activity being stronger during the easterly phase than the westerly phase of the QBO. We assess the representation of this QBO-MJO connection in 30 models participating in the Coupled Model Intercomparison Project 6. While some models reasonably simulate the QBO during boreal winter, none of them capture a difference in MJO activity between easterly and westerly QBO that is larger than that which would be expected from the random sampling of internal variability. The weak signal of the simulated QBO-MJO connection may be due to the weaker amplitude of the QBO than observed, especially between 100 to 50 hPa. This weaker amplitude in the models is seen both in the QBO-related zonal wind and temperature, the latter of which is thought to be critical for destabilizing tropical convection. Plain Language Summary The QBO, which is a dominant mode of interannual variability in the tropical lower stratosphere, has been found to strongly modulate the MJO, a dominant mode of subseasonal variability in the tropical troposphere. We show that while half of the CMIP6 climate models simulate the QBO, none of them capture the observed QBO-MJO relationship. The weak signal of the simulated QBO-MJO relationship may be due to the models' weaker amplitude of the QBO-related wind and temperature signal in the lower stratosphere, which is thought to be critical for modulating the MJO.
Journal Of Geophysical Research: Oceans, May 1, 2015
A simple scheme is developed to represent Sea Surface Diurnal Cycling (SSDC) in Coupled General C... more A simple scheme is developed to represent Sea Surface Diurnal Cycling (SSDC) in Coupled General Circulation Models (CGCM). It follows Zeng and Beljaars [2005], but in addition to a night-time deep well-mixed ocean boundary layer and a deep daytime stable layer, a shallow sub-grid-scale stable diurnal boundary layer is allowed to develop during the day, followed by a deepening convective layer. These four regimes have empirical property profiles and their governing parameters are determined by comparison of idealized experiments with published in situ and satellite observations. Mixing across the base of the shallow stable layer is governed by a gradient Richardson number, so prognostic equations are solved for salinity and current, as well as temperature. A conclusion is that the timing of peak warming depends on diurnal shear. The SSDC is implemented in the Community Earth System Model (CESM) for multiple purposes: the maximum diurnal amplitude of warming is found to exceed 58C and to be more than 28C over most of the ocean; the global distribution of average daytime minus night-time SST is used to validate the SSDC against a satellite SST product; and the mean seasonal surface heat flux and precipitation from an uncoupled CESM atmosphere are used to show the climate impacts that might be expected in a CGCM. Two major conclusions are that these impacts are not negligible and that much of the observed signals of diurnal cycling are captured by SSDC without the computational expense of resolving the relevant ocean processes. Fairall et al. [1996] present a comprehensive treatise on the physics governing these diurnal temperatures and include observations from 42 select days during TOGA COARE. The maximum near-surface diurnal Key Points: Sea surface diurnal cycling has largescale impacts on the atmosphere Diurnal shear is a key factor in the early afternoon peak of surface warming Coupled models can include diurnal cycling schemes at little cost
The North American monsoon (NAM), characterized by distinct seasonal precipitation over western M... more The North American monsoon (NAM), characterized by distinct seasonal precipitation over western Mexico and the Southwestern United States, is a summertime phenomenon that depends on complex interactions between the Pacific Ocean, Gulf of Mexico, and the North American land mass. Thus, the NAM is strongly influenced by the El Niño Southern Oscillation, a dominant mode of interannual Pacific sea surface temperature (SST) and atmospheric variability, as well as the North Pacific Oscillation, a low-frequency (decadal) Pacific variation. This study assesses present day and projected changes in the NAM precipitation on a yearly and seasonal basis. Observations from the NCEP-NCAR Reanalysis project are compared to the Community Climate System Model version 4 (CCSM) from 1980 to 2000. Spatial patterns agree well, but still show an overestimation in precipitation within the NAM region. Fifteen CCSM ensemble runs, for various IPCC AR4 emission scenarios (A2, B1, and constant CO 2), are assessed within each specific scenario and averaged, for comparisons between 1980-2000 and 2080-2100. In the NAM region we find yearly and seasonal decreases in precipitation and increases in temperature for all IPCC emission scenarios. Although this may be, modest RCP's (4.5, 2.6) will have difficulty in detecting a signal above the noise within its scenario. Our analysis further finds statistical significance to the differences in mean precipitation and temperature over the NAM region, due in part to different levels of CO 2 in the atmosphere.
Quantifying sources of subseasonal prediction skill
Subseasonal prediction fills the gap between weather forecasts and seasonal outlooks. There is ev... more 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 snow pack, while predictability from the ocean is attributed to sources such as the El Niño Southern Oscillation. Here we use a unique set of subseasonal reforecast experiments to quantify the respective roles of atmosphere, land, and ocean initial conditions on subseasonal prediction skill over land. 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. 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, challenging our current understanding. However, land-atmosphere coupling is important in week 1. Results are similar over most land regions except South America, where ocean initialization is more important. Subseasonal precipitation prediction skill (weeks 3-6) also comes primarily from the atmosphere initial condition, except for a few regions for which the ocean state becomes important.
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Papers by Julie Caron