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Impacts of a weakened AMOC on precipitation over the Euro-Atlantic region in the EC-Earth3 climate model [1]
['Bellomo', 'Katinka.Bellomo Polito.It', 'Department Of Environment', 'Land', 'Infrastructure Engineering', 'Polytechnic University Of Turin', 'Turin', 'National Research Council', 'Institute Of Atmospheric Sciences', 'Climate']
Date: 2023-07
2.1 Model and experimental design
We carry out model experiments with EC-Earth3, a state-of-the-art GCM participating in the CMIP6 archive and developed by a consortium of European research institutions (Döscher et al. 2022). EC-Earth3 includes the ECMWF IFS cy36r4 atmospheric model, the land-surface scheme H-TESSEL (Balsamo et al. 2009), the NEMO 3.6 ocean model (Madec 2015) and the LIM3 sea-ice component (Rousset et al. 2015). The OASIS3-MCT coupler version 3.0 exchanges fields between the model components (Craig et al. 2017). We use the standard resolutions of TL255L91 for the atmosphere and ORCA1L75 for the ocean (same as in CMIP6). These settings correspond to an atmospheric horizontal resolution of ~ 80 km and 91 vertical levels, and an oceanic horizontal resolution of ~ 100 km and 75 vertical levels.
We run an experiment under preindustrial control conditions (hereafter referred to as ‘control’) for 150 years. In this experiment the external radiative forcing is fixed at the level of representative year 1850, thus all simulated climate variability is internally driven. We compare the control simulation with an experiment in which a freshwater anomaly of 0.3 Sv is uniformly added poleward of 50°N in the Atlantic and the Arctic oceans for 140 years (see Fig. 1). After this time, the hosing is halted and the model is left to freely evolve for an additional 70 years, for a total experiment length of 210 years. Hereafter, this experiment will be referred to as ‘water hosing’. We obtain the freshwater anomaly by applying a virtual salinity flux:
$$F\left(t,x,y\right)=-\frac{h{S}_{o}\left(t,x,y\right)}{{dz}_{0}\left(x,y\right)}$$ (1)
where S 0 is the local salinity in the upper layer, dz 0 is the upper layer thickness, and h is the water hosing field (\(h=\frac{H}{{A}_{R}})\). Here, the denominator (\({A}_{R}\)) is the area of the region in which the water hosing is applied (the North Atlantic and the Arctic in our case), while the numerator (H) is the strength of the freshwater flux anomaly (0.3 Sv = 0.3 \(\bullet\) 106 m3s−1). Then, the following water hosing correction is applied to conserve the total amount of salt throughout the rest of the ocean to the 3D salinity field:
$$\frac{dS\left(t,x,y,z\right)}{dt}=\frac{h\int {S}_{0}\left(t,x,y\right)dxdy}{{V}_{o}}$$ (2)
which represents the total added flux divided by the total ocean volume (\({V}_{o})\).
Fig. 1 Water hosing area. The cyan area shows where the surface freshwater flux anomaly is applied in the water hosing experiment Full size image
We note that the strength of freshwater flux used in this study is deemed unrealistic: however, since current climate models are unable to reproduce AMOC bi-stability and might overestimate AMOC stability (Liu et al. 2017), similar to prior studies we apply a rather large freshwater hosing to force a much weaker AMOC and explore its impacts on the climate system. Note that this forcing has been tested in previous studies (Jackson and Wood 2018), and similar water hosing model simulations of EC-Earth3 were carried out as part of the North Atlantic Hosing Model Intercomparison Project (NAHosMIP, Jackson et al. 2023). In Jackson et al. (2023), the use and validity of water hosing experiments to investigate AMOC mechanisms is further discussed.
2.2 Response of the AMOC
Figure 2a shows the AMOC index in the experiments, defined as the annual mean maximum in the meridional overturning stream-function between 25.5°N and 27.5°N, and below 500 m. The dark purple curve is the AMOC index in the control simulation. The horizontal dark purple line represents the climatological mean value of the AMOC index, which over the span of the control simulation corresponds to 17.66 Sv. The light purple curve represents the AMOC index in the water hosing experiment. The horizontal light purple line represents the value corresponding to a 50% reduction in strength compared to the control climate (8.83 Sv). Also plotted in Fig. 2 is the ocean meridional mass stream-function in the Atlantic/Arctic sector of the control experiment (Fig. 2b), and the average of the years 100 -159 of the water hosing experiment (Fig. 2c). Years 100–159 in the water hosing experiment (shaded in Fig. 2a) are those in which the low-pass filtered (10 years running mean) AMOC index is more than 50% weaker than the control simulation. As expected, the stream-function associated with the AMOC circulation is much reduced in the water hosing experiment (Fig. 2c).
Fig. 2 AMOC diagnostics. a The dark purple curve shows the timeseries of the AMOC strength in the control experiment, while the light purple curve shows the same index but in the water hosing experiment. Superimposed to annual mean values are 10 years running averages of the timeseries. b Mean of the ocean meridional overturning mass stream-function in the Atlantic/Arctic sector for the control experiment; c same as (b) but for the water hosing experiment averaged over the years 100–159 (corresponding to the filled light purple area in panel a). The AMOC strength in panel a is calculated as the maximum strength of the overturning meridional stream-function between 25.5°N and 27.5°N, and below 500 m Full size image
We note that similar to some other GCMs, EC-Earth3 does not simulate a steady decline in high-latitude surface temperature as the AMOC weakens due to hosing, but rather shows periods in which the Arctic warms (in particular, in the first 50 years). We advance an hypothesis for a possible physical mechanism responsible for this in the Supplemental Information (SI). However, here, since the focus is to investigate the effects of a weakened AMOC, for the water hosing experiment we analyze the years 100–159 (shaded in Fig. 2a) in which the AMOC is more than half weaker (< 50%) of its original mean strength. By making this choice, we eliminate from our analysis the possible effects of the transient warm anomalies and sea-ice decrease (fig. S1) on large-scale atmospheric circulation and precipitation patterns (see SI). This leaves us with an adequate sample size (60 years) to compute robust statistical significance against the control experiment at monthly and daily timescales.
2.3 Statistical significance
For all diagnostics, the anomalies are computed with respect to the mean control climate. We retain all 150 years of the control simulation for our calculations. We note that the AMOC (Fig. 2a) as well as global mean surface temperature exhibits centennial timescale fluctuations in EC-Earth3. Meccia et al. (2022) attribute the oscillatory behavior of the AMOC in the EC-Earth3 model to a self-sustained ocean variability. Enhanced OHT, and consequently increased sea-ice melting, are linked to a strong AMOC phase, leading to a build-up of freshwater anomalies in the central Arctic. This anomaly is then released by liquid freshwater transport through the Arctic boundaries, yielding to a freshening of the North Atlantic and a decrease in deep water formation and AMOC strength. A similar mechanism was found by Jiang et al. (2021) for the IPSL-CM6A-LR model, which shares the same ocean model (NEMO) as EC-Earth3, while self-sustained AMOC fluctuations also appear in intermediate complexity models (Mehling et al. 2022).
Centennial AMOC variability in the aforementioned studies is shown to have a large influence on subpolar temperature and sea ice anomalies. In our analysis, we average the control simulation over a period of 150 years, which covers a full AMOC oscillation. Hence, the influence of the centennial fluctuations on the computation of climatological means from the control run is not likely to affect our results. Moreover, the departure of the AMOC in the water hosing experiment from the mean control climate is much larger than the amplitude of the AMOC fluctuations (Fig. 2a). In fact, Meccia et al. (2022) estimated in a 2000 year long preindustrial control run of EC-Earth3 that the average amplitude of centennial fluctuations is ~ 3 Sv, while the departure of the hosed AMOC compared to the control AMOC strength in this study is more than 8.83 Sv.
We further account for the possible influence of the centennial AMOC fluctuations by performing statistical tests. To assess the statistical significance of the averages of the changes in the gridded anomalies (i.e., temperature, precipitation and moisture fields) in the years 100–159 of the water hosing experiment against the control mean climate, we compute averages of 30 years length taken from the control simulation, starting each one of them 5 years apart. Thus, we obtain for each grid point a distribution of 30 averages, which represent the estimated internal variability in the control run. We deem the anomaly in the water hosing experiment statistically significant if it exceeds 2 standard deviations of the distribution of the 30 averages computed from the control simulation. In the figures, we use hashes to indicate where changes are statistically significant according to this test. We tested whether significance is sensitive on the choice of period lengths of 30 years and starting 5 years apart, with different periods ranging from 20 to 60 years and starting them 2, 5, or 10 years apart. We found that our conclusions were not affected. We note that the initial AMOC strength from which the water hosing experiment is started from, may influence the time required by the water hosing to force a weakened AMOC state: however, here we do not focus on mechanisms and timescales of AMOC response to the water hosing forcing (c.f. Jackson et al. 2023), but on its impacts. Finally, although the water hosing anomalies are much larger than the model’s AMOC internal variability, we note that more ensemble members could help corroborate our findings.
2.4 Moisture budget
To investigate mechanisms of precipitation change, we compute the atmospheric moisture budget following D’Agostino and Lionello (2020). A similar moisture budget formulation can also be found in Seager et al. (2010), while a thorough derivation can be found in Trenberth and Guillemot (1995). The moisture budget (precipitation minus evaporation) for the control mean climate can be written as:
$${\rho }_{w}g\left(\overline{P }-\overline{E }\right)=-
abla \bullet {\int }_{0}^{\overline{{p }_{s}}}\left(\overline{{\varvec{u}} }\overline{q })+(\overline{{{\varvec{u}} }^{\boldsymbol{^{\prime}}}{q}^{^{\prime}}}\boldsymbol{ }\right)dp=-{\int }_{0}^{\overline{{p }_{s}}}\left(\overline{{\varvec{u}} }\bullet
abla \overline{q }+\boldsymbol{ }\overline{q }
abla \bullet \overline{{\varvec{u}}}\boldsymbol{ }\right)dp- {\int }_{0}^{\overline{{p }_{s}}}
abla \bullet \left(\overline{{{\varvec{u}} }^{\boldsymbol{^{\prime}}}{q}^{^{\prime}}}\boldsymbol{ }\right)dp - \overline{{q }_{s}{{\varvec{u}}}_{s}\bullet
abla {p}_{s}}$$ (3)
where P and E are the precipitation and evaporation respectively, u is the wind vector, q is specific humidity, p is pressure, \({\rho }_{w}\) is the density of water, and g is the standard gravity. Overbars indicate monthly means, while primes indicate sub-monthly variability. Subscript s indicates values at the surface. For all variables, we use daily data output. The first integral on the RHS of Eq. (3) is the convergence of moisture due to the mean flow, while the second integral on the RHS is the convergence of moisture due to the turbulent flow (transient eddies). We refer to the last term on the RHS as S: this term involves surface quantities representing the deformation of the surface moisture transport by the surface pressure gradient. By computing the moisture budget for both experiments using Eq. (3), the change in moisture budget in the water hosing experiment can be approximated as:
$${\rho }_{w}g\delta \left(\overline{P }-\overline{E }\right)\approx -{\int }_{0}^{\overline{{p }_{s}}}\left(\delta \overline{{\varvec{u}} }\bullet
abla \overline{{q }_{\mathrm{control}}}+\boldsymbol{ }{\overline{{\varvec{u}}} }_{\mathrm{control}}\bullet
abla \delta \overline{q }+\delta \overline{q}\boldsymbol{ }
abla \bullet {\overline{{\varvec{u}}} }_{\mathrm{control}}+\boldsymbol{ }\overline{{q }_{\mathrm{control}}}\boldsymbol{ }
abla \bullet \delta \overline{{\varvec{u}} }\right)dp-{\int }_{0}^{\overline{{p }_{s}}}
abla \bullet \delta \left(\overline{{{\varvec{u}} }^{\mathbf{^{\prime}}}{q}^{\mathrm{^{\prime}}}}\right)dp-{\int }_{0}^{\overline{{p }_{s}}}
abla \bullet \left(\delta \overline{q }\delta \overline{{\varvec{u}} }\right)dp+\delta S$$ (4)
where \(\delta\) indicates the difference between the water hosing and the control experiment. \(\delta S\) is the term involving surface quantities, and tends to be significant in the presence of steep orography. In this analysis, all terms except \(\delta S\) are calculated explicitly. \(\delta S\) is computed as the residual of the other terms.
In the first integral on the RHS of Eq. (4), the contributions of thermodynamics can be separated from those of dynamics: all terms involving a difference in q but not in u are due to thermodynamic processes, while the terms involving a difference in u but not in q are due to dynamic processes. We can rewrite the moisture budget equation as:
$$\delta \left(\overline{P }-\overline{E }\right)\approx \frac{1}{{\rho }_{w}g}(\delta TH+\delta DY+\delta TE+\delta NL+\delta S)$$ (5)
where:
$$\delta TH=-{\int }_{0}^{\overline{{p }_{s}}}
abla \bullet \left(\boldsymbol{ }{\overline{{\varvec{u}}} }_{\mathrm{control}}\delta \overline{q }\right) dp=-{\int }_{0}^{\overline{{p }_{s}}}\left(\boldsymbol{ }{\overline{{\varvec{u}}} }_{\mathrm{control}}\bullet
abla \delta \overline{q }+\delta \overline{q}\boldsymbol{ }
abla \bullet {\overline{{\varvec{u}}} }_{\mathrm{control}}\right) dp$$ (6)
$$\delta DY= -{\int }_{0}^{\overline{{p }_{s}}}
abla \bullet \left(\delta \overline{{\varvec{u}}\boldsymbol{ }}\boldsymbol{ }\overline{{q }_{\mathrm{control}}}\boldsymbol{ }\right) dp=-{\int }_{0}^{\overline{{p }_{s}}}\left(\delta \overline{{\varvec{u}} }\bullet
abla \overline{{q }_{\mathrm{control}}}+\boldsymbol{ }\overline{{q }_{\mathrm{control}}}\boldsymbol{ }
abla \bullet \delta \overline{{\varvec{u}} }\right) dp$$ (7)
$$\delta TE+\delta NL=-{\int }_{0}^{\overline{{p }_{s}}}
abla \bullet \delta \left(\overline{{{\varvec{u}} }^{\boldsymbol{^{\prime}}}{q}^{^{\prime}}}\boldsymbol{ }\right)dp-{\int }_{0}^{\overline{{p }_{s}}}
abla \bullet (\delta \overline{q }\delta \overline{{\varvec{u}} })dp$$ (8)
\(\delta TH\) and \(\delta DY\) represent the thermodynamic and dynamic contributions to the moisture budget, respectively. The transient eddies term \(\delta TE\) = \(-{\int }_{0}^{\overline{{p }_{s}}}
abla \bullet \delta \left(\overline{{{\varvec{u}} }^{\boldsymbol{^{\prime}}}{q}^{^{\prime}}}\boldsymbol{ }\right)dp\) is computed as a covariance, thus it is not straightforward to split this contribution into thermodynamic and dynamic terms. As in previous studies, we leave it as is (c.f. Seager et al. 2010). In this study, because the nonlinear term \(\delta NL=-{\int }_{0}^{\overline{{p }_{s}}}
abla (\delta \overline{q }\delta \overline{{\varvec{u}} })dp\) is small, we incorporate it with the term due to transient eddies. This term contains both thermodynamic and dynamic processes as well.
The term \(\delta TH\) (Eq. 6) represents the change in atmospheric moisture budget that is due to the change in vertically integrated mean humidity. It consists of two components: the first one is the advection of moisture \(\delta {TH}_{A}=-{\int }_{0}^{\overline{{p }_{s}}}\left(\boldsymbol{ }{\overline{{\varvec{u}}} }_{\mathrm{control}}\bullet
abla \delta \overline{q }\right) dp\), and is related to the change in the humidity gradient (e.g., land-sea contrast) along the mean flow. The second component is defined as \(\delta {TH}_{D}=-{\int }_{0}^{\overline{{p }_{s}}}\left(\boldsymbol{ }\delta \overline{q}\boldsymbol{ }
abla \bullet {\overline{{\varvec{u}}} }_{\mathrm{control}}\right) dp\), and is related to the change in mean humidity in areas of mean flow convergence (upward air motion) or divergence (downward air motion). The term \(\delta DY\) (Eq. 7) represents the change in atmospheric moisture budget that is due to the change in the mean flow. Similar to \(\delta TH\), it consists of two components: the first advective component \(\delta {DY}_{A}=-{\int }_{0}^{\overline{{p }_{s}}}\left(\delta \overline{{\varvec{u}} }\bullet
abla \overline{{q }_{\mathrm{control}}}\right) dp\) is related to the change in mean wind flow in the presence of spatial gradients of humidity. The second component \(\delta {DY}_{D}=-{\int }_{0}^{\overline{{p }_{s}}}\left(\overline{{q }_{\mathrm{control}}}\boldsymbol{ }
abla \bullet \delta \overline{{\varvec{u}} }\right) dp\) is the convergence or divergence of moisture due to the change in the mean flow. The terms of the moisture budget computed for the control climate of EC-Earth3 from Eq. (3) are consistent with previously published results, including residuals (e.g., Seager et al. 2010, D’Agostino and Lionello 2020), and will not be discussed here. A review of possible sources of errors in the computation of the moisture budget can be found in Seager and Henderson (2013). Finally, we note that the moisture budget framework does not take into account precipitation recycling (Brubaker et al. 1993). Precipitation recycling contributes to the moisture budget over land areas, and tends to be less important when considering large-scale processes such in the present study: it would be nonetheless possible in a future work to further disentangle mechanisms of precipitation changes over land using a moisture tracking formulation.
2.5 Weather regimes
To further characterize the mechanisms of precipitation change, we investigate the association of precipitation anomalies with wintertime (DJFM) Euro-Atlantic weather regimes (WRs) (e.g., Hannachi et al. 2017; Straus et al. 2017; Dawson et al. 2012). In our study, WRs are computed using clustering as described by Fabiano et al. (2020). Following their methods, we calculate WRs from daily geopotential height at 500 hPa (hereafter z500) in the Euro-Atlantic sector (30°N–90°N, 80°W–40°E). We obtain z500 daily anomalies (z500’) by removing the daily mean seasonal cycle smoothed with a 20-day running mean. Since WRs are related to large-scale atmospheric circulation, to reduce complexity arising from variability at small scales, we first compute the first 4 EOFs of the z500’, which explain ~ 55% variance in the control simulation.
We then apply K-means clustering on the principal components (PCs) associated with these EOFs to derive 4 cluster centroids. Each centroid corresponds to a WR. Each day in the control simulation is then assigned to the closest centroid based on the minimum Euclidean distance. We obtain patterns of z500’ associated with each WR by compositing over all days in each cluster. In agreement with a large number of previous studies (e.g., Vautard 1990; Michelangeli et al. 1995; Cassou 2008; Dawson et al. 2012; Strommen et al. 2019; Fabiano et al. 2020), we set the a priori number of centroids (i.e., WRs) to be 4, corresponding to the positive phase of the North Atlantic Oscillation (NAO +), Scandinavian Blocking (SBL), negative phase of the North Atlantic Oscillation (NAO−), and Atlantic Ridge (AR). The z500’ composites associated with each WRs in the control simulation are shown and discussed in Sect. 3.4. Fabiano et al. (2020) found that all climate models show biases in the simulation of WRs in the historical period, nevertheless WRs are quite consistent with reanalyses. EC-Earth3 was found to lie well within the inter-model spread (Fabiano et al. 2021).
We compute WRs in the water hosing experiment assigning each day to one of the 4 centroids obtained from the control simulation. The z500’ in the water hosing experiment are obtained with respect to the control climatology. To avoid the chance that changes in the WRs in the water hosing simulation are caused by a change in z500, rather than changes in variability, we linearly detrend the northern hemisphere z500 at each grid point (c.f. fig. S3), although we note that detrending does not alter our results. The z500’ anomalies are then projected onto the 4 EOFs computed from the control simulation, thereby producing 4 pseudo-PCs. Each day in the water hosing experiment is then assigned to a centroid based on the minimum Euclidean distance between the 4 pseudo-PCs and the control centroids. Statistical significance of the difference in the number of days and persistence in each WRs with respect to the control simulation is computed using the 99% probability threshold of the Welch’s t-test.
We note that to test the robustness of our choices, we tried setting the variance explained by the EOFs to be 80% in the control experiment to obtain the reduced dimensionality space, instead of choosing the a priori number of EOFs to be 4. With this choice, we obtain a total number of 12 EOFs, but we find that our results are not affected. Here, WRs in the water hosing experiment are computed using the control climatology as reference to compare the changes in atmospheric circulation patterns with respect to the control experiment. Computing WRs in the water hosing experiment using the water hosing climatology itself as reference, leads to largely similar WRs patterns. However, the question of how the spatial patterns may change in the new water hosed climate is not specifically addressed here.
[END]
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[1] Url:
https://link.springer.com/article/10.1007/s00382-023-06754-2
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