NP3.2

Greenland Block Index (GBI) and North Atlantic Oscillation (NAO) are climate indices widely used for climatological studies especially over the Greenland Ice Sheet (GrIS). Particularly in summer, they are highly and negatively correlated; both have a strong relationship to near surface processes around the GrIS; their magnitude creates non-linear feedbacks and influences the low troposphere, shaping spatial accumulation and ablation patterns.

NAO is a measure of the surface pressure difference over the North Atlantic, providing insight of intensity and location of the jet stream. GBI denotes the general circulation over Greenland at the 500-hPa level and depending on its signal promotes heat and moist advection towards inland.

Based on the 1959-2019 period, the extreme summer melt of 2019 recorded the highest mean summer GBI while the extreme summer melt of 2012 recorded the lowest mean summer NAO. Their impact, however, goes beyond the melting season since the inter-seasonal phase change of these two indices may enhance/ postpone early melt/late refreezing and vice-versa.

Supported by 62 years of high-resolution regional climate model output (RACMO2.3p2), this work uses a statistical approach to analyze inter-seasonal variability of climate oscillations and their impact on the surface energy budget components over the GrIS. Also, teleconnection changes in a changing climate are hypothesized.

How to cite: Silva, T., Abermann, J., Shahi, S., Schöner, W., and Nöel, B.: Is the impact of climate oscillations changing over the Greenland Ice Sheet?, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14893, https://doi.org/10.5194/egusphere-egu21-14893, 2021.

Pseudo-proxy experiments test the skill and sensitivity of two extended climate field reconstruction (CFR) methodologies in reconstructing northern North Atlantic Summer sea surface temperatures (SSTs). The Summer target data originate from one millennium-long simulation of the CESM LME (Otto-Bliesner et al. 2016) .  The experiments test the reconstruction skill systematically for input data mimicking SST marine proxies and instrumental observations, including characteristics such as sparse distribution in space, varying signal-to noise ratio and age uncertainties.

The Bayesian hierarchical model BARCAST assumes implicitly that the target variable is described as an AR(1) process in time (Tingley & Huybers 2010), while the proxy surrogate reconstruction (PSR) method makes no such assumption (Graham et al. 2007). The PSR selects climate analogues from the simulated instrumental period in our study.

Results show that both methodologies generate skillful reconstructions for perfectly dated input data, and the PSR is superior when realistic noise levels are chosen for the input data. When the input is perturbed with age-uncertainties, the methodologies are unable to generate acceptable skillful reconstructions. Facilitating in form of data clustering is tested for both methodologies in the attempt of improving reconstruction skill. This proves successful for the PSR methodology, with the best skill obtained using n=3 clusters over the reconstruction region.

Additionally, and addressed as a topic for discussion, we detect weak temporal persistence in the input data and the BARCAST reconstructions. The lack of SST persistence is found to be partly due to the input data sampling frequency: Summer means (June, July, August) averaged for every year. Analyses show that the simulated SST data exhibit weaker memory from one Summer to the next, compared to year-to-year variability based on annual means. Similar results are also found for instrumental observations. This finding stands in contrast to results of previous studies on terrestrial reconstruction, where climate reconstructions and individual proxy records exhibit strong persistence properties, also targeting the Summer season (Werner et al. 2018, Nilsen et al. 2018)^,.

References:

Graham, N.E. et al. (2007), Clim. Change, 83, 241-285, doi: 10.1007/s10584-007-9239-2

Otto-Bliesner, B.L. et al (2016), Bull. Amer. Meteor. Soc., 97, 735-754, doi: 10.1175/BAMS-D-14-00233.1

 Nilsen, T. et al. (2018), Clim. Past, 14, 947-967, doi: 10.5194/cp-14-947-2018

Tingley, M. P. and P. Huybers (2010a), J. Clim., 23, 2759–2781, doi: 10.1175/2009JCLI3015.1

Werner, J. P. et al. (2018), Clim. Past, 14, 527-557, doi: 10.5194/cp-14-527-2018

How to cite: Nilsen, T. and Talento, S.: Climate field reconstruction of North Atlantic sea surface temperatures, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9865, https://doi.org/10.5194/egusphere-egu21-9865, 2021.

There is emergent evidence that abrupt shifts of the Atlantic Meridional Overturning Circulation (AMOC) have occurred during interglacial periods, with recent observations and model simulations showing that we may have over-estimated its stability during warm climates. In this study, we present a multi-proxy reconstruction of deep-water characteristics from the Rockall Trough in the Eastern North Atlantic to assess the variability of Nordic seas and Labrador Sea deep-water formation during past interglacial periods MIS 1, 5, 11, and 19. To test the warm climate stability hypothesis and to constrain the variability of deep-water formation for past warm climates, we performed geochemical analysis on planktic (Nd isotopes) and benthic foraminifera (δ¹⁸O and δ¹³C) along with sedimentological analysis. This approach allows us to reconstruct paleocurrent flow strength, as well as the origin and contribution of different water masses to one of the deep-water components of the AMOC in the Rockall Trough. We found that deep-water properties varied considerably during each of our chosen periods. For example during the Holocene εNd variability is smaller (1.8 per mil) when compared to variability during MIS 19 (3.3 per mil), an interglacial that experienced very similar orbital boundary conditions. Our results confirm that deep-water variability in the eastern North Atlantic basin was more variable in previous interglacial periods when compared to our current Holocene and provide new insight into the relative contribution of Nordic Seas Deep Water and Labrador Sea Water in the Rockall trough.

How to cite: Murphy O' Connor, M., Colin, C., and Morley, A.: Assessing the stability of the AMOC during past warm climates, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15298, https://doi.org/10.5194/egusphere-egu21-15298, 2021.

One of the main contributors to palaeoclimate variability on millennial timescales is understood to be Dansgaard-Oeschger (D-O) cycles. Our awareness of these phenomena arises primarily from quasi-periodic, abrupt transitions of large magnitude detected in δ¹⁸O records from Greenland ice cores (e.g. Dansgaard et al, 1982; Johnsen et al, 1992), although there is evidence of similar variability in other archives and regions. D-O cycles have plenty to capture the imagination:

• the strength and rapidity of climate changes over Greenland,

• their regularity throughout MIS3 (~60 to 30 thousand years before present) and occurrence during the last deglaciation contrasting with their relative absence during the Last Glacial Maximum and Holocene,

• their opposed characteristics in Greenland and Antarctica,

• and that different models require different boundary conditions to reproduce this phenomena, if they can reproduce it at all.

This talk characterises D-Olike cycles in two different models: Planet Simulator (PlaSim, an Earth System Model with simplified atmospheric physics, thermodynamic sea ice, and simplified ocean dynamics), and COSMOS (a CMIP3-era ESM). We identify four phases to D-O cycles and commonalities and differences in their representations in these models. Finally, we examine which phases of this type of variability continue to contribute to climate variability today and what that looks like.

How to cite: Andres, H. and Tarasov, L.: Characterising Dansgaard-Oeschger cycles: from MIS3 to today, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13778, https://doi.org/10.5194/egusphere-egu21-13778, 2021.

To describe earth’s former and predict the expected future climate in a general way we need to understand at least two basic characteristics of the distribution of earth’s temperature, its mean state and its temporal and spatial variance of temperature. There is some confidence in the projection of the mean state but the characteristics and changes of climate variability, especially on multi-decadal and longer time-scales are less known.

To characterize climate variability on these time scales, the instrumental record is too short. Climate proxies such as oxygen isotopes from foraminifera retrieved from marine sediments provide long records but do not exclusively carry information about the climate signal of interest. The decomposition of proxy time series into climate and non-climate components is challenging and depends on the adequate representation of the major involved biological and physical processes influencing the record. But even with a reasonable representation of the combined processes as fluctuations in proxy seasonality, bioturbation and errors in the age model, a proxy record still appears as the combination of these effects.

As a proxy record is only a single representation of this sum of effects we work on replicate measurements as a tool to characterize and separate the variability components. We therefore analysed oxygen isotopes and Mg/Ca in replicated measurements from the same sample, in replicated samples from the same sediment layer and in nearby sediment cores spanning the Holocene.  
If we compare two records the relation of them will determine the commonness of the underlaying processes. As records for example come from the same core or from cores of nearby located sites, they share the same climate signal. In the case they are from the same core they also share the errors in the age model and the time uncertainty introduced by bioturbation. Combining different types of replicates allows us the analyse the effect of different combinations of shared and independent errors.

The first two cores that we work on come from about 10 km apart located sites in the Indonesian Sea. GeoB 10054-4 was drilled in a water depths of 1076 meters, at longitude of 112°40.10’E and latitude 8°40.90’S and its average sedimentation rate was estimated as 20 cm/kyears. GeoB 100537 was drilled in a water depths of 1372 meters, at longitude 112°52.30’E and at latitude 8°40.56’S and its average sedimentation rate is estimated as 45cm/kyears.

In the presentations we will show first results of the analysis of intra core and inter core variability.

How to cite: Dyck, H., Laepple, T., Dolman, A., Groeneveld, J., and Mohtadi, M.: Separating Climate Variability and Non-Climate Noise in Proxy Records - A case study on replicate marine sediment records, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13495, https://doi.org/10.5194/egusphere-egu21-13495, 2021.

Synoptic timescale forecasts over Equatorial Africa are important for averting weather-and climate-related disasters and the resulting agricultural losses. Observational studies have shown that rainfall anomalies often propagate eastward across Equatorial Africa, and that there is a linkage between synoptic-scale eastward-propagating precipitation and Convectively Coupled Kelvin Waves (CCKWs) over this region. We explore the mechanisms in which CCKWs modulate the propagation of precipitation from West to East over Equatorial Africa. We examine the first Africa-wide climate simulation from a convection permitting model (CP4A) along with its global driving-model simulation (G25) and evaluate both against observations (TRMM) and ERA-Interim (ERA-I), with a focus on precipitation and Kelvin wave activity.

Lagged composites show that both simulations capture the eastward propagating precipitation signal seen in observational studies, though G25 has a weaker signal. Composite analysis on high-amplitude Kelvin waves further shows that both simulations capture the connection between the eastward propagating precipitation anomalies and Kelvin waves. In comparison to TRMM, however, the precipitation signal is weaker in G25, while CP4A is more realistic. As the Kelvin wave activity is also well represented in both simulations when compared to ERA-I, the weak precipitation signal in G25 may be partly associated with the weak coupling between the precipitation and Kelvin waves. We show that CCKWs modulate the eastward propagation of convection and precipitation across Equatorial Africa through at least two related physical processes. Firstly, an enhancement of the low-level westerlies leads to increased low-level convergence; secondly, westerly moisture flux anomalies amplify lower-to-mid-tropospheric specific humidity. Results show that both CP4A and G25 generally simulate the key horizontal features of CCKWs, with anomalous low-level westerlies in phase with positive precipitation anomalies. However, both models show a weakness in capturing the vertical profile of anomalous specific humidity, and the zonal-vertical circulation is too weak in G25 and incoherent in CP4A compared to ERA-I.

In both ERA-I and the simulations, Kelvin wave-induced convergence and the westward tilt with height of anomalous zonal winds and specific humidity tends to weaken to the east of the East African highlands. It appears that these highlands impede the coherent eastward propagation of the wave-precipitation coupled structure.

How to cite: Ayesiga, G., Holloway, C., Williams, C., Yang, G.-Y., Stratton, R., and Roberts, M.: Linking Equatorial African precipitation to Kelvin wave processes in the CP4-Africa convection-permitting regional climate simulation, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8419, https://doi.org/10.5194/egusphere-egu21-8419, 2021.

The North Atlantic Oscillation (NAO) is a high-frequency oscillation that has known influences on the climatology of weather patterns across the eastern United States. This study explores the relationship between the daily North Atlantic Oscillation index with observed high-wind events from 391 first-order weather stations across the eastern U.S. from 1973-2015. These events were determined following typical National Weather Service high-wind criteria: sustained winds of at least 18 m•s-1 for at least 1 hour or a wind gust of at least 26 m•s-1 for any duration. Since research literature shows high-wind events are often connected to parent mid-latitude cyclone tracks, and since the NAO has been shown to influence these storm tracks, it is hypothesized that changes in NAO phases are connected to spatial shifts and frequencies in high-wind observations. Initial results show a preferred southwesterly direction during each NAO phase. Variance in high-wind directions appears to increase (decrease) during negative (positive) NAO phases. Further, the greatest spatial difference in the mean center of high-wind observations was between positive and negative NAO phases. Overall, these preliminary findings indicate changes in high-wind observations may be linked to NAO phases.

How to cite: Stinnett, S., Durkee, J., Gilliland, J., Murley, V., Black, A., and Goodrich, G.: The Relationship between the North Atlantic Oscillation and High-Wind Observations Across the Eastern United States, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13792, https://doi.org/10.5194/egusphere-egu21-13792, 2021.

Earth's climate can be understood as a dynamical system that changes due to external forcing and internal couplings. It can be characterized from the evolution of essential climate variables, such as surface air temperature. Yet, the mechanisms, amplitudes, and spatiotemporal patterns of global and local temperature fluctuations around its mean, called temperature variability, are insufficiently understood. Discrepancies exist between temperature variability from model and paleoclimate data at the temporal scale of years to centuries and at the local scale, both of which are important socio-economic scales for long-term planning.
Here, we clarify whether global and local temperature signals from the last millennia show a stationary variance on these timescales and thus behave in a stable manner or not. Therefore, we contrast power spectral densities and their scaling behaviors using simulated, observed, and reconstructed temperatures on periods between 10 and 200 years. Despite careful consideration of possible spectral biases, we find that local temperatures from paleoclimate data tend to show unstable behavior, while simulated temperatures almost exclusively show stable behavior. Conversely, the global mean temperature tends to be stable. We explain this by introducing the gain as a powerful tool to analyze the forced temperature response, based on a novel estimate of the joint power spectrum of radiative forcing.
Our analysis identifies main deficiencies in the properties of temperature variability and offers new insights into the linkage between raditative forcing and temperature response, relevant to the understanding of Earth’s dynamics and the assessment of climate risks.

How to cite: Ellerhoff, B. and Rehfeld, K.: Timescale-dependent stability of surface air temperature and the forced temperature response, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1939, https://doi.org/10.5194/egusphere-egu21-1939, 2021.

The highly successful Budyko-Sellers energy balance models are based on the classical continuum mechanics heat equation in two spatial dimensions. When extended to the third dimension using the correct conductive-radiative surface boundary conditions, we show that surface temperature anomalies obey the (nonclassical) Half-order energy balance equation (HEBE, with exponent H = ½) implying heat is stored in the subsurface with long memory. 

Empirically, we find that both internal variability and the forced response to external variability are compatible with H ≈ 0.4.  Although already close to the HEBE and classical continuum mechanics, we argue that an even more realistic “effective media” macroweather model is a generalization: the fractional heat equation (FHE) for long-time (e.g. monthly scale anomalies).  This model retains standard diffusive and advective heat transport but generalize the (temporal) storage term.  A consequence of the FHE is that the surface temperature obeys the Fractional EBE (FEBE), generalizing the HEBE to 0< H ≤1.  We show how the resulting FEBE can be been used for monthly and seasonal forecasts as well as for multidecadal climate projections.  We argue that it can also be used for understanding and modelling past climates.

How to cite: Lovejoy, S.: Budyko-Sellers 2.0: the classical and fractional heat equations, and the fractional energy balance equation, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7877, https://doi.org/10.5194/egusphere-egu21-7877, 2021.

Natural climate variability is partially attributed to the solar radiative forcing. The scope of this work is to increase the scientific understanding of the relative role of solar variations on the terrestrial climate. The applied methodology examines initially the variation of multiple climatic parameters (temperature, zonal wind, relative and specific humidity, sensible and latent surface heat flux, cloud cover, precipitation) in response to the 11-year solar cycle. An additional goal is to estimate the response of the climate system’s parameters to the solar forcing in multiple forecasting horizons and to evaluate the behavior of the climate system in shorter time scales. The adopted methodology includes the development of linear regression models which calculate the dependency of the climatic parameters to solar variations for each grid point of the global dataset on a monthly time scale. The solar indicator used in this study is the 10.7-cm solar radio flux (F10.7) provided by NOAA, while the climate data are extracted from the NCEP/NCAR Reanalysis 1 project with a spatial resolution of 2.5^o X 2.5^o for 67 years. Regarding the climate system’s response forecasting, an Artificial Neural Network has been trained for modeling and forecasting the solar indicator time series for a few time steps in advance and the effect on climatic parameters is estimated using the established regression equations. The results exhibit that the variation of the climatic parameters can be partially attributed to the 11-year solar cycle. Statistically significant areas with relatively high solar cycle signal were found in multiple pressure levels and geographical regions. Furthermore, the results indicate that the identification of a clear solar signal in the climatic data is a difficult task due to the climate system’s complexity; advanced non-linear methods could be applied in order to obtain a more accurate understanding of this research field.

How to cite: Tzanis, C. G., Benetatos, C., and Philippopoulos, K.: The impact of solar variability on climatic parameters, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3000, https://doi.org/10.5194/egusphere-egu21-3000, 2021.

What is the spatial scale of climate fluctuations, and how does this scale depend on the timescale under consideration? To answer this question, the spatio-temporal correlation structure of global surface temperature fields is characterized, for the period 1850-present, by estimating frequency spectra of the effective spatial degrees of freedom (ESDOF). These ESDOF spectra serve as a simple summarizing metric of the frequency-dependent spatial auto-correlation function. ESDOF spectra are estimated from: (a) the HadCRUT global gridded temperature anomaly dataset, based exclusively on instrumental measurements, and including detailed error variance estimates; (b) the NOAA 20th Century Reanalysis; and (c) a large ensemble of CMIP historical climate model simulations. When comparing (i) error corrected ESDOF spectra from the instrumental data to (ii) those obtained from the reanalysis and the model simulations, with HadCRUT data gaps imposed, results are found to be highly consistent among the three data sources. When the analysis is applied to the entire globe, the ESDOF spectra exhibit an almost uniform power-law frequency scaling with about 100 ESDOFs at monthly timescales and only about 2 ESDOFs at multidecadal timescales. Second-order differences in this scaling behaviour are found when the analysis is restricted to various spatial subdomains of the globe, namely, the tropics, extra-tropics, land areas, and ocean areas. A few implications of the diagnosed ESDOF reduction towards the longer timescales are briefly discussed.

How to cite: Kunz, T. and Laepple, T.: Power-law frequency scaling of surface temperature spatial degrees of freedom – estimated from instrumental data, reanalysis and climate model simulations, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13589, https://doi.org/10.5194/egusphere-egu21-13589, 2021.